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: Systems and methods of detecting and correcting redeye in an image are described. In one aspect, pixels of the input image are segmented based on projections of color values of the pixels onto two-dimensional thresholding planes. Candidate redeye pixel areas are identified in the input image based on the segmented pixels of the input image.

Abstract: The disclosure relates to a system and a method for generating clothing feature data representative of at least one clothing feature of a piece of clothing being worn by the person in a set of images, and training a discriminative clothing classifier using the clothing feature data to provide a personal clothing model that corresponds to the piece of clothing. The personal clothing model can be used to identify additional images in which the person appears.

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: A method for characterizing an arrangement of template regions includes identifying a first bounding box that includes one or more template regions, identifying a second bounding box of one or more template regions that are not in the first bounding box, organizing the first and second bounding boxes into a new bounding box, and designating the new bounding box as the characterized arrangement of template regions.

Abstract: Systems and methods of classifying pixels into a nonhuman animal integument class are described. In one aspect, values of features in a discriminant space are determined for respective pixels of the input image. The pixels are classified into a nonhuman animal integument class based on the respective feature values and a mapping that segments the discriminant space into one or more regions of feature values representing pixels associated with a nonhuman animal integument characteristic and one or more regions of feature values representing pixels unassociated with the nonhuman animal integument characteristic.

Abstract: Peteye is the appearance of an unnatural coloration (not necessarily red) of the pupils in an animal appearing in an image captured by a camera with flash illumination. Systems and methods of detecting and correcting peteye are described. In one aspect a classification map segmenting pixels in the input image into peteye pixels and non-peteye pixels is generated based on a respective segmentation condition on values of the pixels. Candidate peteye pixel areas are identified in the classification map. The generating and the identifying processes are repeated with the respective condition replaced by a different respective segmentation condition on the pixel values.

Abstract: Peteye is the appearance of an unnatural coloration (not necessarily red) of the pupils in an animal appearing in an image captured by a camera with flash illumination. Systems and methods of detecting and correcting peteye are described. In one aspect a classification map segmenting pixels in the input image into peteye pixels and non-peteye pixels is generated based on a respective segmentation condition on values of the pixels. Candidate peteye pixel areas are identified in the classification map. The generating and the identifying processes are repeated with the respective condition replaced by a different respective segmentation condition on the pixel values.

Abstract: Method, apparatus, and systems for extracting features from face regions and auxiliary identification regions of images are described. In one aspect, a face region containing at least a portion of a person's face is detected in an input image. An auxiliary identification region outside the face region is identified in the input image. Facial features characterizing the face region are determined. Auxiliary identification features characterizing the auxiliary identification region are calculated. An indexing datum is derived from the facial features and the auxiliary identification features. The indexing datum is stored in association with the input image.

Abstract: In one aspect, a metadata-based classification result is obtained based on metadata associated with an image. A histogram-based classification result is determined based on a histogram of intensity values derived from the image. The image is classified into a scene type class based on the metadata-based classification result and the histogram-based classification result. In another aspect, a first condition is applied on a first one of metadata associated with an image corresponding to a measure of amount of light received in capturing the image to obtain a first metadata-based classification result. A second condition is applied on a second one of the metadata corresponding to a measure of brightness of the image to obtain a second metadata-based classification result. The image is classified into a scene type class based on the first and second metadata-based classification results.

Abstract: Systems and methods of detecting and correcting redeye in an image are described. In one aspect, the input image is sub-sampled to generate a thumbnail image, redeye pixel areas are detected in the thumbnail image. In another aspect, an input image having lines of pixels with original color values is processed. One or more redeye pixel areas corresponding to respective areas in the input image are detected. Each pixel in the input image corresponding to the detected redeye pixel areas is classified as a redeye pixel or a non-redeye pixel on a line-by-line basis without reference to pixels in adjacent lines. The original color values of pixels in the input image classified as redeye pixels are corrected.

Abstract: Using a joint video and still image pipeline technology, a network may be deployed through the Internet so that a user may acquire a remote high resolution photograph using a low resolution video streamed from a remote camera as a view-finder. The remote camera may enable the user to exercise control from a remote device through the network or other communication channels. A network server may store the video and the high resolution photograph, and either sends the video and the high resolution photograph to the user upon payment or posts the video and the high resolution photograph on a web page.

Abstract: Systems and methods of detecting and correcting redeye in an image are described. In one aspect, pixels of the input image are segmented based on projections of color values of the pixels onto two-dimensional thresholding planes. Candidate redeye pixel areas are identified in the input image based on the segmented pixels of the input image.

Abstract: Method, apparatus, and systems for extracting features from face regions and auxiliary identification regions of images are described. In one aspect, a face region containing at least a portion of a person's face is detected in an input image. An auxiliary identification region outside the face region is identified in the input image. Facial features characterizing the face region are determined. Auxiliary identification features characterizing the auxiliary identification region are calculated. An indexing datum is derived from the facial features and the auxiliary identification features. The indexing datum is stored in association with the input image.

Abstract: Systems and methods of detecting and correcting redeye in an image are described. In one aspect, pixels of the input image are segmented based on projections of color values of the pixels onto two-dimensional thresholding planes. Candidate redeye pixel areas are identified in the input image based on the segmented pixels of the input image.

Abstract: Systems and methods of classifying pixels into a nonhuman animal integument class are described. In one aspect, values of features in a discriminant space are determined for respective pixels of the input image. The pixels are classified into a nonhuman animal integument class based on the respective feature values and a mapping that segments the discriminant space into one or more regions of feature values representing pixels associated with a nonhuman animal integument characteristic and one or more regions of feature values representing pixels unassociated with the nonhuman animal integument characteristic.

Abstract: An exemplary system for browsing videos comprises a memory for storing a plurality of videos, a processor for accessing the videos, and a video browsing user interface for enabling a user to browse the videos. The user interface is configured to enable video browsing in multiple states on a display screen, including a first state for displaying static representations of the videos, a second state for displaying dynamic representations of the videos, and a third state for playing at least a portion of a selected video.

Abstract: In one aspect, a metadata-based classification result is obtained based on metadata associated with an image. A histogram-based classification result is determined based on a histogram of intensity values derived from the image. The image is classified into a scene type class based on the metadata-based classification result and the histogram-based classification result. In another aspect, a first condition is applied on a first one of metadata associated with an image corresponding to a measure of amount of light received in capturing the image to obtain a first metadata-based classification result. A second condition is applied on a second one of the metadata corresponding to a measure of brightness of the image to obtain a second metadata-based classification result. The image is classified into a scene type class based on the first and second metadata-based classification results.

Abstract: Peteye is the appearance of an unnatural coloration (not necessarily red) of the pupils in an animal appearing in an image captured by a camera with flash illumination. Systems and methods of detecting and correcting peteye are described. In one aspect a classification map segmenting pixels in the input image into peteye pixels and non-peteye pixels is generated based on a respective segmentation condition on values of the pixels. Candidate peteye pixel areas are identified in the classification map. The generating and the identifying processes are repeated with the respective condition replaced by a different respective segmentation condition on the pixel values.

Abstract: A method for producing a set of images as a photobook is provided. The method includes selecting a set of images having meta data and determining an attribute value for an attribute associated with each of the images. After determining an attribute value of an attribute, the method adjusts the attribute value associated with at least one of the images to render the attribute value of the images uniform. The method then organizes the images automatically using the meta data and determines a layout of the photobook by automatically adjusting spatial characteristics of the images within the photobook. Once the method determines a layout of the photobook, the method outputs the photobook.