Abstract: Systems and methods to generate a motion attention model of a video data sequence are described. In one aspect, a motion saliency map B is generated to precisely indicate motion attention areas for each frame in the video data sequence. The motion saliency maps are each based on intensity I, spatial coherence Cs, and temporal coherence Ct values. These values are extracted from each block or pixel in motion fields that are extracted from the video data sequence. Brightness values of detected motion attention areas in each frame are accumulated to generate, with respect to time, the motion attention model.

Abstract: An implementation of a technology, described herein, for relevance-feedback, content-based facilitating accurate and efficient image retrieval minimizes the number of iterations for user feedback regarding the semantic relevance of exemplary images while maximizing the resulting relevance of each iteration. One technique for accomplishing this is to use a Bayesian classifier to treat positive and negative feedback examples with different strategies. In addition, query refinement techniques are applied to pinpoint the users' intended queries with respect to their feedbacks. These techniques further enhance the accuracy and usability of relevance feedback. This abstract itself is not intended to limit the scope of this patent. The scope of the present invention is pointed out in the appending claims.

Abstract: An implementation of a technology, described herein, for relevance-feedback, content-based facilitating accurate and efficient image retrieval minimizes the number of iterations for user feedback regarding the semantic relevance of exemplary images while maximizing the resulting relevance of each iteration. One technique for accomplishing this is to use a Bayesian classifier to treat positive and negative feedback examples with different strategies. In addition, query refinement techniques are applied to pinpoint the users' intended queries with respect to their feedbacks. These techniques further enhance the accuracy and usability of relevance feedback. This abstract itself is not intended to limit the scope of this patent. The scope of the present invention is pointed out in the appending claims.

Abstract: An image retrieval system performs both keyword-based and content-based image retrieval. A user interface allows a user to specify queries using a combination of keywords and examples images. Depending on the input query, the image retrieval system finds images with keywords that match the keywords in the query and/or images with similar low-level features, such as color, texture, and shape. The system ranks the images and returns them to the user. The user interface allows the user to identify images that are more relevant to the query, as well as images that are less or not relevant to the query. The user may alternatively elect to refine the search by selecting one example image from the result set and submitting its low-level features in a new query. The image retrieval system monitors the user feedback and uses it to refine any search efforts and to train itself for future search queries.

Abstract: A system and method that includes a virtual human face generation technique which synthesizes images of a human face at a variety of poses. This is preferably accomplished using just a frontal and profile image of a specific subject. An automatic deformation technique is used to align the features of a generic 3-D graphic face model with the corresponding features of these pre-provided images of the subject. Specifically, a generic frontal face model is aligned with the frontal image and a generic profile face model is aligned with the profile image. The deformation procedure results in a single 3-D face model of the specific human face. It precisely reflects the geometric features of the specific subject. After that, subdivision spline surface construction and multi-direction texture mapping techniques are used to smooth the model and endow photometric detail to the specific 3-D geometric face model.

Abstract: Methods and systems for generic adaptive multimedia content delivery are described. In one embodiment, a novel framework features an abstract content model and an abstract adaptive delivery decision engine. The abstract content model recognizes important aspects of contents while hiding their physical details from other parts of the framework. The decision engine then makes content adaptation plans based on the abstracted model of the contents and needs little knowledge of any physical details of the actual contents. Thus, under the same framework, adaptive delivery of generic contents is possible.

Abstract: Methods and systems for generic adaptive multimedia content delivery are described. In one embodiment, a novel framework features an abstract content model and an abstract adaptive delivery decision engine. The abstract content model recognizes important aspects of contents while hiding their physical details from other parts of the framework. The decision engine then makes content adaptation plans based on the abstracted model of the contents and needs little knowledge of any physical details of the actual contents. Thus, under the same framework, adaptive delivery of generic contents is possible.

Abstract: Systems and methods to generate a motion attention model of a video data sequence are described. In one aspect, a motion saliency map B is generated to precisely indicate motion attention areas for each frame in the video data sequence. The motion saliency maps are each based on intensity I, spatial coherence Cs, and temporal coherence Ct values. These values are extracted from each block or pixel in motion fields that are extracted from the video data sequence. Brightness values of detected motion attention areas in each frame are accumulated to generate, with respect to time, the motion attention model.

Abstract: A portion of an audio signal is separated into multiple frames from which one or more different features are extracted. These different features are used, in combination with a set of rules, to classify the portion of the audio signal into one of multiple different classifications (for example, speech, non-speech, music, environment sound, silence, etc.). In one embodiment, these different features include one or more of line spectrum pairs (LSPs), a noise frame ratio, periodicity of particular bands, spectrum flux features, and energy distribution in one or more of the bands. The line spectrum pairs are also optionally used to segment the audio signal, identifying audio classification changes as well as speaker changes when the audio signal is speech.

Abstract: A method and system for learning-based assessment of the quality of an image is provided. An image quality assessment system trains an image classifier based on a training set of sample images that have quality ratings. To train the classifier, the assessment system generates a feature vector for each sample image representing various attributes of the image. The assessment system may train the classifier using an adaptive boosting technique to calculate a quality score for an image. Once the classifier is trained, the assessment system may calculate the quality of an image by generating a feature vector for that image and applying the trained classifier to the feature vector to calculate the quality score for the image.

Abstract: A portion of an audio signal is separated into multiple frames from which one or more different features are extracted. These different features are used, in combination with a set of rules, to classify the portion of the audio signal into one of multiple different classifications (for example, speech, non-speech, music, environment sound, silence, etc.). In one embodiment, these different features include one or more of line spectrum pairs (LSPs), a noise frame ratio, periodicity of particular bands, spectrum flux features, and energy distribution in one or more of the bands. The line spectrum pairs are also optionally used to segment the audio signal, identifying audio classification changes as well as speaker changes when the audio signal is speech.

Abstract: Systems and methods for clustering Web queries are described. In one aspect, one or more of a same document and a plurality of similar documents selected by a user in response to a plurality of queries is identified. Responsive to this identification, a query cluster is generated. The cleric the query cluster indicates that the queries are similar independent of whether individual ones of the queries comprise similar composition with respect to other ones of the queries.

Abstract: Methods and systems for generic adaptive multimedia content delivery are described. In one embodiment, a novel framework features an abstract content model and an abstract adaptive delivery decision engine. The abstract content model recognizes important aspects of contents while hiding their physical details from other parts of the framework. The decision engine then makes content adaptation plans based on the abstracted model of the contents and needs little knowledge of any physical details of the actual contents. Thus, under the same framework, adaptive delivery of generic contents is possible.

Abstract: Red-eye detection based on red region detection with eye confirmation initially identifies pixels that correspond to the color of red-eye within an image. A determination is then made as to whether these identified pixels and surrounding areas are part of an eye or not part of an eye. Those identified pixels that are determined to be part of an eye are the detected red-eye regions.

Abstract: A method and system for generating a classifier to classify sub-objects of an object based on a relationship between sub-objects is provided. The classification system provides training sub-objects along with the actual classification of each training sub-object. The classification system may iteratively train sub-classifiers based on feature vectors representing the features of each sub-object, the actual classification of the sub-object, and a weight associated with the sub-object. After a sub-classifier is trained, the classification system classifies the training sub-objects using the trained sub-classifier. The classification system then adjusts the classifications based on relationships between training sub-objects. The classification system assigns a weight for the sub-classifier and weight for each sub-object based on the accuracy of the adjusted classifications.

Abstract: Methods and systems for identifying black frames within a sequence of frames are provided. In one embodiment, the detection system detects black frames within a sequence of frames by fully decoding base frames and then partially decoding non-black, non-base frames in a way that ensures the blackness of each frame can be determined. The detection system decodes base frames before decoding dependent frames, which is referred to as processing frames in reverse order of dependency since a frame is processed before the frames that depend on it are processed. In another embodiment, the detection system determines the blackness of frames within a sequence of frames by processing the frames in order of their dependency and following chains of block dependency to decode and determine the blackness of blocks.

Abstract: A system and methods analyze music to detect musical beats and to rectify beats that are out of sync with the actual beat phase of the music. The music analysis includes onset detection, tempo/meter estimation, and beat analysis, which includes the rectification of out-of-sync beats.

Abstract: A system and methods use music features extracted from music to detect a music mood within a hierarchical mood detection framework. A two-dimensional mood model divides music into four moods which include contentment, depression, exuberance, and anxious/frantic. A mood detection algorithm uses a hierarchical mood detection framework to determine which of the four moods is associated with a music clip based on the extracted features. In a first tier of the hierarchical detection process, the algorithm determines one of two mood groups to which the music clip belongs. In a second tier of the hierarchical detection process, the algorithm then determines which mood from within the selected mood group is the appropriate, exact mood for the music clip. Benefits of the mood detection system include automatic detection of music mood which can be used as music metadata to manage music through music representation and classification.

Abstract: A system that analyzes music to detect musical beats and to rectify beats that are out of sync with the actual beat phase of the music. The music analysis includes onset detection, tempo/meter estimation, and beat analysis, which includes the rectification of out-of-sync beats.

Abstract: A system and methods use music features extracted from music to detect a music mood within a hierarchical mood detection framework. A two-dimensional mood model divides music into four moods which include contentment, depression, exuberance, and anxious/frantic. A mood detection algorithm uses a hierarchical mood detection framework to determine which of the four moods is associated with a music clip based on the extracted features. In a first tier of the hierarchical detection process, the algorithm determines one of two mood groups to which the music clip belongs. In a second tier of the hierarchical detection process, the algorithm then determines which mood from within the selected mood group is the appropriate, exact mood for the music clip. Benefits of the mood detection system include automatic detection of music mood which can be used as music metadata to manage music through music representation and classification.