Abstract: Systems and methods to automatically edit a video to generate a video summary are described. In one aspect, sub-shots are extracted from the video. Importance measures are calculated for at least a portion of the extracted sub-shots. Respective relative distributions for sub-shots having relatively higher importance measures as compared to importance measures of other sub-shots are determined. Based on the determined relative distributions, sub-shots that do not exhibit a uniform distribution with respect to other sub-shots in the particular ones are dropped. The remaining sub-shots are connected with respective transitions to generate the video summary.

Abstract: Systems and methods to generate a video summary of a video data sequence are described. In one aspect, key-frames of the video data sequence are identified independent of shot boundary detection. A static summary of shots in the video data sequence is then generated based on key-frame importance. For each shot in the static summary of shots, dynamic video skims are calculated. The video summary consists of the calculated dynamic video skims.

Abstract: Systems and methods to generate an attention model for computational analysis of video data are described. In one aspect, feature components from a video data sequence are extracted. Attention data is generated by applying multiple attention models to the extracted feature components. The generated attention data is integrated into a comprehensive user attention model for the computational analysis of the video data sequence.

Abstract: A method comprising receiving media content and analyzing one or more attributes of successive shots of the received media. Based, at least in part on the analysis of the one or more attributes, generating a correlation score for each of the successive shots, wherein scene segmentation is performed to group semantically cohesive shots.

Abstract: Methods and apparatuses are provided for detecting blur within digital images using wavelet transform and/or Cepstrum analysis blur detection techniques that are able to detect motion blur and/or out-of-focus blur.

Abstract: A face detection system and process capable of detecting a person depicted in an input image and identifying their face pose. Prepared training images are used to train a 2-stage classifier which includes a bank of Support Vector Machines (SVMs) as an initial pre-classifier layer, and a neural network forming a subsequent decision classifier layer. Once the SVMs and the neural network are trained, input image regions are prepared and input into the system. An output is produced from the neural network which indicates first whether the region under consideration depicts a face, and secondly, if a face is present, into what pose range the pose of the face falls.

Abstract: Various embodiments provide methods and systems for streaming data that can facilitate streaming during bandwidth fluctuations in a manner that can enhance the user experience. In one aspect, a forward-shifting technique is utilized to buffer data that is to be streamed, e.g. an enhancement layer in a FGS stream. Various techniques can drop layers actively when bandwidth is constant. The saved bandwidth can then be used to pre-stream enhancement layer portions. In another aspect, a content-aware decision can be made as to how to drop enhancement layers when bandwidth decreases. During periods of decreasing bandwidth, if a video segment does not contain important content, the enhancement layers will be dropped to keep the forward-shifting of the enhancement layer unchanged. If the enhancement layer does contain important content, it will be transmitted later when bandwidth increases.

Abstract: A video skim is assembled by identifying one or more key frames from a video shot. Certain lengths of frames to the left and right of the key frame are measured for visual content variety. Depending upon the measured visual content variety to the left and right of the key frame, the video skim is assembled that has L frames to the left of the key frame and R frames to the right of the key frame. Measuring the visual content variety to the left and right of the key frame, provides a video skim that incorporates the more salient features of a shot.

Abstract: The disclosed subject matter improves iterative results of content-based image retrieval (CBIR) using a bigram model to correlate relevance feedback. Specifically, multiple images are received responsive to multiple image search sessions. Relevance feedback is used to determine whether the received images are semantically relevant. A respective semantic correlation between each of at least one pair of the images is then estimated using respective bigram frequencies. The bigram frequencies are based on multiple search sessions in which each image of a pair of images is semantically relevant.

Abstract: The described subject matter provides systems and procedures to make query similarity determinations, wherein the queries are used in information retrieval operations. A same document and/or multiple similar documents are identified that have been selected by a user in response to multiple queries. Responsive to identifying the same document and/or the similar documents, a query cluster is generated that indicates that the queries used to obtain the same and/or similar documents. This is accomplished in a manner that is independent of whether individual ones of the queries are compositionally similar with respect to other ones of the queries.

Abstract: An apparatus is provided for detecting blur in an image. The apparatus includes an image generator that is configured to generate a plurality of corresponding different resolution images based on a base image. The plurality of corresponding different resolution images is provided to an edge detector. The edge detector detects edge transitions in each of the plurality of corresponding different resolution images and provides edge maps to an edge parameter comparator. The edge parameter comparator compares corresponding edge parameters as detected by the edge detector and provides a result map to a blur calculator. The blur calculator determines at least one blur parameter based on the result map and provides the blur parameter to a blur detector. The blur detector then determines if the base image is blurred based on a comparison of the blur parameter with at least one blur parameter threshold.

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: The described arrangements and procedures provide an intelligent media agent to autonomously collect semantic multimedia data text descriptions on behalf of a user whenever and wherever the user accesses media content. The media agent analyzes these semantic multimedia data text descriptions in view of user behavior patterns and actions to assist the user in identifying multimedia content and related information that is appropriate to the context within which the user is operating or working. For instance, the media agent detects insertion of text and analyzes the inserted text. Based on the analysis, the agent predicts whether a user intends to access media content. If so, the agent retrieves information corresponding to media content from a media content source and presents the information to a user as a suggestion.

Abstract: The described arrangements and procedures identify an image's orientation by extracting features from peripheral portions of the image. The procedure evaluates the extracted features based on training image feature orientation classification models to identify the image's orientation.

Abstract: By understanding a website author's intention through an analysis of the function of a website, website content can be adapted for presentation or rendering in a manner that more closely appreciates and respects the function behind the website. Various inventive systems and methods analyze a website's function so that its content can be adapted to different client environments, e.g. devices, network conditions, or user preferences. A novel function-based object model automatically identifies objects associated with a website, and analyzes those objects in terms of their functions. The function-based object model permits consistent, informed decisions to be made in the adaptation process, so that web content is displayed not only in an organized manner, but in a manner that reflects the author's intention.

Abstract: A system and process for video characterization that facilitates video classification and retrieval, as well as motion detection, applications. This involves characterizing a video sequence with a gray scale image having pixel levels that reflect the intensity of motion associated with a corresponding region in the sequence of video frames. The intensity of motion is defined using any of three characterizing processes. Namely, a perceived motion energy spectrum (PMES) characterizing process that represents object-based motion intensity over the sequence of frames, a spatio-temporal entropy (STE) characterizing process that represents the intensity of motion based on color variation at each pixel location, a motion vector angle entropy (MVAE) characterizing process which represents the intensity of motion based on the variation of motion vector angles.

Abstract: An apparatus is provided for detecting blur in an image. The apparatus includes an image generator that is configured to generate a plurality of corresponding different resolution images based on a base image. The plurality of corresponding different resolution images is provided to an edge detector. The edge detector detects edge transitions in each of the plurality of corresponding different resolution images and provides edge maps to an edge parameter comparator. The edge parameter comparator compares corresponding edge parameters as detected by the edge detector and provides a result map to a blur calculator. The blur calculator determines at least one blur parameter based on the result map and provides the blur parameter to a blur detector. The blur detector then determines if the base image is blurred based on a comparison of the blur parameter with at least one blur parameter threshold.

Abstract: A method comprising receiving media data in a compressed, digital domain, analyzing motion vectors associated with the received media content while in the compressed digital domain and identifying one or more objects in the received media data based, at least in part, on the motion vector analysis.

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: Text features corresponding to pieces of media content (e.g., images, audio, multimedia content, etc.) are extracted from media content sources. One or more text features (e.g., one or more words) for a piece of media content are extracted from text associated with the piece of media content and text feature vectors generated therefrom and used during subsequent searching. Additional low-level feature vectors may also be extracted from the piece of media content and used during the subsequent searching. Relevance feedback can also be received from a user(s) identifying the relevance of pieces of media content rendered to the user in response to his or her search request. The relevance feedback is logged and can be used in determining how to respond to subsequent search requests, such as by modifying feature vectors (e.g., text feature vectors) corresponding to the pieces of media content for which relevance feedback is received.