Abstract: A system and method for placing spine groups within a display is provided. A shape is defined within a display. Groups of cluster spines are placed circumferentially around the defined shape. Overlap between at least two of the spine groups is identified. One of the overlapping spine groups is rotated in one of a clockwise and anticlockwise direction away from the other overlapping spine group. Overlap between the rotated spine group and at least one of the other overlapping spine group and another placed spine group is identified. The rotated overlapping spine group is moved in an outward direction away from the shape.

Abstract: An image processing system comprises an image processor configured to identify a plurality of candidate boundaries in an image, to obtain corresponding modified images for respective ones of the candidate boundaries, to apply a mapping function to each of the modified images to generate a corresponding vector, to determine sets of estimates for respective ones of the vectors relative to designated class parameters, and to select a particular one of the candidate boundaries based on the sets of estimates. The designated class parameters may include sets of class parameters for respective ones of a plurality of classes each corresponding to a different gesture to be recognized. The candidate boundaries may comprise candidate palm boundaries associated with a hand in the image. The image processor may be further configured to select a particular one of the plurality of classes to recognize the corresponding gesture based on the sets of estimates.

Abstract: Embodiments are provided for organization and presentation of content. In some embodiments, a plurality of images and a plurality of similarity rules for image categorization are received. For each image in the plurality of images, that image and each remaining image from the plurality is compared by: applying each similarity rule to the image and a remaining image from the plurality to obtain a numeric result, and recording the numeric result for the pair of images in a numeric representation, the numeric representation embodying similarities. The numeric representation is used as a reference for clustering the plurality of images into clusters of similar images, and each image is stored with a marker denoting a cluster to which it has been assigned.

Abstract: An activity recognition method, for recognizing continuous activities of several moving objects in the foreground of a video, includes: capturing and processing a training video to get a contour of a moving object; extracting a minimum bounding box of the contour in order to get parameters then transfer to feature vectors; constructing a decision tree model based on support vector machines (SVMs), for classifying the activities of the moving object according to the parameter and the feature vector of the training video; capturing and processing a testing video to get other parameters and using several formulas to generate feature vectors, and executing an algorithm for recognizing the activities of several moving objects in the foreground of the testing video. Said feature vectors are transformed from the parameters that in the testing and training videos. Via above descriptions, the method can recognize activities of foreground objects in the testing video.

Abstract: An image evaluation apparatus is provided, which calculates a characteristic value indicating a state of appearance of objects corresponding to person a and person b appearing in image A and an object corresponding to person b appearing in image B. Subsequently, the image evaluation apparatus specifies person b? as the photographer of image B and calculates a likelihood degree indicating accuracy of the determination. Further, the image evaluation apparatus calculates an importance degree of each of images A and B according to the characteristic values of person a and person b appearing in image A and image B and the likelihood degree of the photographer of image B.

Abstract: Multimodal data mining in a multimedia database is addressed as a structured prediction problem, wherein mapping from input to the structured and interdependent output variables is learned.

Abstract: A system and a method are disclosed that determine images with co-occurrence groups of individuals from an image collection. A value of a similarity metric is computed for each pair of images of the image collection, the value of the similarity metric being computed based on a comparison of the number of individuals in common between the images of the pair and the total number of individuals identified in both images of the pair. The collection of images is clustered based on the computed values of the similarity metric. At least one co-occurrence group is determined based on the results of the clustering, where a co-occurrence group is determined as a cluster of images that have a similar combination of individuals.

Abstract: Embodiments of a system and method for automatic creation of a multimedia presentation or highlight collection from a collection of candidate contents are generally described herein. In some embodiments, each one of a plurality of videos or images in the candidate contents are automatically evaluated for quality, content, metadata, and desirability based on user specified inclusion factors. Inclusion factors may be utilized to generate one or more scores for the candidate contents, which provide for automatic ranking of the candidate contents. Based on scores generated from the selected inclusion factor criteria a highlight collection of images is automatically generated. The highlight collection can be included in a multimedia presentation, in the form of a memory book, slideshow, or digital narrative, and can be automatically generated from the plurality of videos or images.

Abstract: Methods for selecting metrics for substrate classification, and apparatus to perform such methods. The methods include determining a value of a metric from an image of a substrate sample for each substrate sample of a plurality of substrate samples, wherein the metric is indicative of a surface texture of each substrate sample and iteratively assigning substrate samples of the plurality of substrate samples to an aggregate of a particular number of aggregates in response to a value of the metric for each substrate sample until a convergence of clustering is deemed achieved, then determining an indication of cluster tightness of the particular number of aggregates. The methods further include selecting or ignoring the metric for substrate classification in response to the indication of cluster tightness of the particular number of aggregates.

Abstract: An image processing apparatus includes: an extraction unit that extracts a first image and a second image similar to the first image, in a first resolution; and a generation unit that generates an image in a second resolution based on the respective images extracted by the extraction unit and phases of the respective images calculated with precision higher than one pixel in the first resolution.

Abstract: An image identifying device includes a mechanism that sets an evaluation area whose category is to be identified in an in-vivo image; a mechanism that acquires texture components from the evaluation area in the in-vivo image; a mechanism that calculates an evaluation value indicating homogeneity of the texture components; and a mechanism that identifies the category of the evaluation area on the basis of the evaluation value.

Abstract: In one implementation, a computer-implemented method includes receiving, at a computer system, an electronic photograph; and identifying, by the computer system, a plurality of users of depicted in the electronic photograph. The computer-implemented method can also include designating a group of users based on the identified plurality of users; and providing information regarding the designated group of users to one or more computing devices associated with one or more of the plurality of users.

Abstract: An image recognition device that improves the accuracy of generic object recognition compared with conventional technologies by reducing the influence of the position, size, background clutter and the like of an object that is targeted to be recognized in the input image by the generic object recognition. The image recognition device performs a generic object recognition and includes: a segmenting unit configured to segment an input image into a plurality of regions in accordance with meanings extracted from content of the input image; a generating unit configured to compute feature data for each of the plurality of regions and generate feature data of the input image reflecting the computed feature data; and a checking unit configured to check whether or not a recognition-target object is present in the input image in accordance with the feature data of the input image.

Abstract: Embodiments generally relate to summarizing a photo album in a social network system. In one embodiment, a method includes grouping photos into a plurality of groups of photos, and selecting a plurality of representative photos, where each representative photo represents a respective group from the plurality of groups, where the selecting is based on a quality score of each of the photos, and where each quality score is based on different types of attributes. The method also includes enabling the plurality of representative photos to be shared.

Abstract: Image fragments are formed in regions corresponding to circles searched from an input image. In a cascade of homogeneous classifiers, each classifier classifies input vectors corresponding to the image fragments into a face type and a non-face type. This procedure is performed on all images included in an image pyramid and the coordinates of a face detected based on the results of the procedures on all images.

Abstract: Methods, systems, and articles of manufacture for presenting similar images are disclosed. A method for presenting similar images on a display device is disclosed. The method includes displaying a first image on the display device; determining one or more homographic relationships between the first image and a plurality of images; identifying, using the determined one or more homographic relationships, at least one image having a scene and a perspective which are similar to that of the first image; and displaying the identified image. Corresponding system and computer readable media embodiments are also disclosed.

Abstract: A method for fine-grained image classification on an image includes automatically segmenting one or more objects of interest prior to classification; and combining segmented and original image features before performing final classification.

Abstract: Various methods for clustering images of faces are provided. One example method may include receiving a plurality of input clusters including a first cluster and at least a second cluster, where each input cluster including at least one feature that was extracted from an image. The example method may further include determining a first medoid for the first cluster and a second medoid for the second cluster, determining an inter-cluster distance between the first medoid and the second medoid, and merging the first cluster with the second cluster to generate a merged cluster in an instance in which the inter-cluster distance is less than a distance threshold. Similar and related example methods, example apparatuses, and example computer program products are also provided.

Abstract: Intuitive photo grouping is accomplished utilizing photo metadata information, including photos' timestamps, GPS information, name and storage folder identity, to automatically generate logical and meaningful event photo groupings for users.

Abstract: Techniques are presented for analyzing audio-video segments, usually from multiple sources. A combined similarity measure is determined from text similarities and video similarities. The text and video similarities measure similarity between audio-video scenes for text and video, respectively. The combined similarity measure is then used to determine similar scenes in the audio-video segments. When the audio-video segments are from multiple audio-video sources, the similar scenes are common scenes in the audio-video segments. Similarities may be converted to or measured by distance. Distance matrices may be determined by using the similarity matrices. The text and video distance matrices are normalized before the combined similarity matrix is determined. Clustering is performed using distance values determined from the combined similarity matrix.

Abstract: A nearest-neighbor-based distance metric learning process includes applying an exponential-based loss function to provide a smooth objective; and determining an objective and a gradient of both hinge-based and exponential-based loss function in a quadratic time of the number of instances using a computer.

Abstract: The disclosure is related to a system and method for learning robust clothing clustering based on a cluster ensemble technique applied to the clothing features of images to improve clustering of images. Different types of clothing features that are complementary to each other are computed to provide extensive description of the clothing in the images. Multiple partitions are computed based on the clothing features to generate a cluster ensemble set. A consensus function is applied to the multiple partitions to generate a final clothing consensus clustering that encompasses the information contained in the multiple partitions. A system and method are disclosed for clustering images based on the clothing of one or more persons in the images.

Abstract: The invention relates to a method for detecting clusters of biological particles (12) on a surface (11), comprising steps that involve: a. determining (E1) a topographical representation (20) of said surface; and b. detecting (E3, E4), on said topographical representation, at least one contour defining a region that is likely to correspond to a cluster of biological particles.

Abstract: A method and apparatus for encoding an image is provided. An image coding unit, including a region that deviates from a boundary of a current picture, is divided to obtain a coding unit having a smaller size than the size of the image coding unit, and encoding is performed only in a region that does not deviate from the boundary of the current picture. A method and apparatus for decoding an image encoded by the method and apparatus for encoding an image is also provided.

Abstract: A method and apparatus for encoding an image is provided. An image coding unit, including a region that deviates from a boundary of a current picture, is divided to obtain a coding unit having a smaller size than the size of the image coding unit, and encoding is performed only in a region that does not deviate from the boundary of the current picture. A method and apparatus for decoding an image encoded by the method and apparatus for encoding an image is also provided.

Abstract: A method and apparatus for encoding an image is provided. An image coding unit, including a region that deviates from a boundary of a current picture, is divided to obtain a coding unit having a smaller size than the size of the image coding unit, and encoding is performed only in a region that does not deviate from the boundary of the current picture. A method and apparatus for decoding an image encoded by the method and apparatus for encoding an image is also provided.

Abstract: An image processing apparatus, which can automatically arrange a plurality of images, includes a rectangle generation unit configured to divide the plurality of images into two or more groups and generate, for each group, a rectangle in which images of the group can be arranged. The image processing apparatus includes a determination unit configured to generate a new rectangle by combining two of the rectangles generated by the rectangle generation unit, in which a rectangle composed of the plurality of groups is generated in such a way as to locate all frames of the plurality of images in the rectangle, and further configured to determine a layout of frames of the plurality of images. The image processing apparatus further includes an image generation unit configured to generate an image of the plurality of images disposed in one rectangle based on the layout of the frames of the plurality of images determined by the determination unit.

Abstract: Embodiments are provided for organization and presentation of content. In some embodiments, a plurality of images and a plurality of similarity rules for image categorization are received. For each image in the plurality of images, that image and each remaining image from the plurality is compared by: applying each similarity rule to the image and a remaining image from the plurality to obtain a numeric result, and recording the numeric result for the pair of images in a numeric representation, the numeric representation embodying similarities. The numeric representation is used as a reference for clustering the plurality of images into clusters of similar images, and each image is stored with a marker denoting a cluster to which it has been assigned.

Abstract: Described is a linear structure from motion technique that is scalable, parallelizable, treats images equally, and is robust to outliers, without requiring intermediate bundle adjustment. Camera rotations for images are estimated using feature point correspondence and vanishing points matched across the images. The camera rotation data is fed into a linear system for structure and translation estimation that removes outliers and provides output data corresponding to structure from motion parameters. The data may be used in further optimization e.g. with a final non-linear optimization stage referred to as bundle adjustment to provide final refined structure from motion parameters.

Abstract: Scene-based people metering for audience measurement is disclosed. Example methods disclosed herein include grouping successive image frames depicting a location of a media presentation to form a sequence of scenes, respective scenes including respective groups of the image frames. Such example methods also include grouping matching scenes into respective scene clusters having respective sizes, the scene clusters being represented by respective key frames. Such example methods further include assigning respective ranks to the key frames of the respective scene clusters, the respective ranks being determined based on the respective sizes of the scene clusters. Such example methods additionally include processing the key frames in accordance with the respective ranks to monitor an audience of the media presentation.

Abstract: An adaptive, interactive visual workspace for viewing groups of files based on their relationships. Relationships of files are visualized using iterative refinement of categories through a direct-manipulation graph-based layout. The visual workspace starts with a fully connected graph linking thumbnail images of related files that is then partitioned into neighborhoods in response to a user creating file stacks corresponding to different categories. Normalized spring lengths improve the overall quality of the layout. Different modes for membership in neighborhoods avoid confusing motion of files and help a user to manually organize the workspace. Additionally, retrieved files can be added without having to significantly move the previous files. Different visualization techniques indicate which files are related to each other. Different zoom rates are used for file location, and surrogate sizes allow users to increase the separation between files while still increasing the surrogate sizes.

Abstract: To reduce a display time of an image for which an observation is less required, and to effectively perform an observation of a series of images, the image display apparatus (1) includes an image processing controller (2a) that acquires an image from a storage unit (5), controls various image processes for the acquired image, and stores an image of a processing result in the storage unit (5), an image classification unit (2b) that calculates a correlation value between temporally continuous images and classifies each of the images into an image group based on the calculated correlation value, an image-of-interest detecting unit (2c) that detects a feature-image area including a predetermined feature from each of the images, and detects the feature image including the detected feature-image area as an image-of-interest, a representative-image extracting unit (2d) that extracts the image-of-interest and a first image in each of the image groups as a representative image and sets a display rate for each of the ex

Abstract: A method for processing data includes receiving a depth map of a scene containing a humanoid form. The depth map is processed so as to identify three-dimensional (3D) connected components in the scene, each connected component including a set of the pixels that are mutually adjacent and have mutually-adjacent depth values. Separate, first and second connected components are identified as both belonging to the humanoid form, and a representation of the humanoid form is generated including both of the first and second connected components.

Abstract: A sort-out cycle such as a judgment of three levels of “usable”, “unusable”, and “reserve” is made to images and, when the judgment of all images is completed, the judgment of the three levels is made again to the “reserve” images is repeated. The number of sort-out cycle times in which the judgment is made is applied as an evaluation to the images which are finally determined as “usable”.

Abstract: The classification and segmentation system of the current invention makes use of information from pixels of an image, namely the magnitude of the pixels, to run specific analytics to classify and segment the image pixels into different groups. This invention includes a system for processing an image, the system including an input device, a processor, a memory and a monitor. The input device is configured to receive image data, where the image data includes pixels and each pixel has a magnitude. The memory has instructions stored in it that, when executed by the processor, cause the processor to run calculations. The calculations include: calculating the log-magnitudes from the magnitudes of at least a plurality of the pixels, calculating standard deviations of the log-magnitudes for subsets of the plurality of pixels and compute an integral of the standard deviations over a desired range. The pixels are classified into different groups based on a value of the integral relative to one or more integral values.

Abstract: Provided are an age estimation apparatus, an age estimation method, and an age estimation program capable of reducing the labor of labeling the image data used for age estimation. An age estimation apparatus for estimating an age of a person on image data includes a dimension compression unit for applying dimension compression to the image data to output low dimensional data; a clustering unit for performing clustering of the low dimensional data outputted; a labeling unit for labeling representative data of each cluster among the low dimensional data clustered; and an identification unit for estimating an age of a person on the basis of a learning result using a feature amount contained in labeled low dimensional data and unlabeled low dimensional data.

Abstract: An image retrieval method comprising: a step of extracting at least one query feature vector from a query image on which a subject of the image retrieval is captured, the query feature vector representing a local feature of the query image; a step of accessing an image data base in which a plurality of reference images are stored previously, each reference image being stored in conjunction with learning images generated therefrom and reference feature vectors representing local features of the reference image and the learning images; a comparing step of comparing the query feature vector with the reference feature vectors stored in conjunction with each reference image using an approximate nearest neighbor search to find a reference feature vector approximately nearest to the query feature vector; and a selecting step of selecting a reference image with which the found reference feature vector is stored in conjunction from the reference images as a retrieval result wherein: the learning image is generated by

Abstract: Disclosed is a method of determining one or more event subsets within a plurality of images. Each image is associated with time and location data specifying the time and location of capture of the image. The method determines a time variable for each adjacent pair of images in a capture time ordered list of the plurality of images. A distance variable for each adjacent pair of images in the ordered list of images is then determined. The method determines speed data of the image capture device at the time and location of capture of each image. The ordered list of images is then partitioned into one or more event subsets on the basis of a cost function, the cost function being determined in accordance with a normalization of the time variable and distance variable, wherein the time variable and the distance variable are weighted relative to the speed data.

Abstract: An object recognition system and method is provided which uses automated algorithmically determined negative training. Negative training with respect to a particular object classifier allows for more streamlined and efficient targeted negative training, enabling time and cost savings while simultaneously improving the accuracy of recognition based on the targeted negative training.

Abstract: A method and system for clustering the pixels of a multispectral image is provided. A clustering system computes a maximum spectral similarity score for each pixel that indicates the similarity between that pixel and the most similar neighboring. To determine the maximum similarity score for a pixel, the clustering system generates a similarity score between that pixel and each of its neighboring pixels and then selects the similarity score that represents the highest similarity as the maximum similarity score. The clustering system may apply a filtering criterion based on the maximum similarity score so that pixels with similarity scores below a minimum threshold are not clustered. The clustering system changes the current pixel values of the pixels in a cluster based on an averaging of the original pixel values of the pixels in the cluster.

Abstract: A method of detecting recurring events in a digital image collection taken over a pre-determined period of time is disclosed. The method uses a processor for analyzing the digital image collection to produce a two-dimensional representation of the distribution of image capture activity over time and detecting recurring events by identifying spatial clusters in the two-dimensional representation.

Abstract: The described implementations relate to computer vision. In one case image data is received. Resource constraints associated with processing the image data are evaluated. Further, a visual recognition and detection processing strategy is selected for the image data based at least in part on the evaluated resource constraints.

Abstract: A method of processing digital images by transforming a set of pixels from a three-dimensional space to a normalized two-dimensional space, determining a membership class and membership class level of each pixel in the set of pixels, and selectively modifying colors of pixels in the set of pixels based on the determined membership classes and membership class levels.

Abstract: In some embodiments, information associated with a first plurality of image pairs is received. Each image pair is assessed to detect visual pairwise differences that qualify as an error. A visual pairwise difference may be a difference with respect to at least one of position, size, color, or style. A prediction engine is trained based upon the assessed visual pairwise differences. Information associated with a second plurality of image pairs is received. Each of these image pairs comprises at least a portion of a visual end-user experience screen of an event-driven application executed in a client-tier environment. Each of these image pairs is assessed, using the prediction engine, to detect visual pairwise differences that qualify as an error. User feedback is received, indicating that at least one assessed pairwise difference should not have qualified as an error. The prediction engine is then re-trained based on the user feedback.

Abstract: Systems, methods, and computer storage mediums are provided for matching multiple photographs together. An example method includes receiving a first collection of photographic images. The photographic images in the first collection are clustered into one or more composite sets of photographic images based on a comparison of the metadata associated with each photographic image in the first collection meeting a predetermined similarity threshold. An image overlap is determined between each photographic image within each of the one or composite sets of photographic images. When the image overlap exceeds a predetermined image overlap threshold, a pair of photographic images are matched, for all of the photographic images within each composite set of photographic images to form one or more composite images.

Abstract: A method and apparatus for tracking a cluster of objects. A path for a center of mass for the cluster of objects is identified using a sequence of frames of sensor data generated by a sensor system. The path is identified with respect to a coordinate system for the sensor data. A relative path for each object in the cluster of objects is identified with respect to the coordinate system for the sensor data. The relative path is relative to the path for the center of mass for the cluster of objects. A final path for each object in the cluster of objects is identified with respect to the coordinate system for the sensor data using the path for the center of mass for the cluster of objects and the relative path for each object in the cluster of objects.

Abstract: A computer-implemented system and method for organizing cluster groups within a display is provided. Cluster groups each having one or more spines of clusters are obtained. Each cluster includes at least one document. At least one of the cluster groups is placed around a ring centrally defined in a display. Circle sectors are defined around the ring and an initial target angle is identified within each of the sectors. The at least one cluster group is positioned within one of the circle sectors at the initial target angle. A further one of the cluster groups is positioned within a different circle sector up to one of a maximum and minimum angle relative to the initial target angle for that sector.

Abstract: Systems and methods are disclosed to categorize images by detecting local features for each image; applying a tree structure to index local features in the images; and extracting a rank list of candidate images with category tags based on a tree indexing structure to estimate a label of a query image.

Abstract: Various embodiments of the invention provide systems and methods for classifying physical documents that have been converted to digital documents. Specifically, some embodiments are configured to classify digital documents that belong to a document classification whose representative members that lack structure or have varying structure, either of which makes automatic classification of such documents using conventional methods difficult. For example, certain systems and methods according to the invention can be used to classify physical real estate documents that have been converted to digital real estate documents, especially those that lack a discernable document structure.

Abstract: A method includes accessing a digital image including a plurality of faces including a first face and a second face. The method includes identifying a plurality of identification regions of the digital image including a first identification region associated with the first face and a second identification region associated with the second face. The method also includes assigning the digital image to a first face cluster of a plurality of face clusters when a difference between data descriptive of the first identification region and data descriptive of a face cluster identification region of the first face cluster satisfies a threshold. The method further includes assigning the digital image to a second face cluster of the plurality of face clusters based at least partially on a frequency of the second face and the first face appearing together in an image.