Abstract: The present invention provides a method for providing augmented product specifications based on user reviews. The method obtains input data of specifications and user reviews on a plurality of products, each specification including at least a pair of a feature and a feature-value of the product. The method concatenates the user reviews of the products to form product-documents, each product-document corresponding to the concatenated user reviews of a product. The method further employs a topic model to process the input data and learn topic distributions in the product-documents and word distributions in topics. The topics include specifications of the products. The topic model is a type of statistical model for discovering topics that occur in a collection of product-documents. Based on the topic model, the method can provide augmented specifications including one or more of relevant sentences of the feature-value, feature importance information, and product-specific words of the product.

Abstract: The present invention provides a salience-based media manipulation method and system for inserting contents into video presentations. The method includes establishing an advertisement-element pool containing a plurality of advertisement elements provided for insertion; obtaining an input video presenting at least one scenes and containing a plurality of original frames; and performing shot grouping and primary scene analysis on the input video to acquire scenes with static background and limited camera motion. The method also includes detecting white-space candidates in the acquired scenes for inserting at least one advertisement element and adjusting alignment between the white-space candidates and the at least one advertisement element. The adjusted alignment increases a salience gain of the advertisement elements and limit salience change of the scenes without insertion.

Abstract: A face recognition method is provided. The method includes dividing an input video into different sets of frames and detecting faces of each frame in the input video. The method also includes generating face tracks for the whole video. Further, the method includes applying a robust collaborative representation-based classifier to recover a clean image from complex occlusions and corruptions for a face test sample and perform classification. In addition, the method also includes outputting the video containing the recognized face images.

Abstract: A method is provided for content alignment. The method includes obtaining a first content sequence and a second content sequence different from the first content sequence. The method also includes representing each of the first content sequence and the second content sequence in a hierarchical structure containing an ordered root element sequence and a sub-tree structure. The ordered root element sequence includes a plurality of root elements and each root element is associated with a sub-tree of elements. The method also includes determining a desired alignment between the first content sequence and the second content sequence using dynamic programming, and outputting results of the desired alignment between the first content sequence and the second content sequence.

Abstract: A face recognition method is provided to use sparse representation and regularized least squares-based classification on a computing device. The method includes obtaining an image to be recognized as a test sample y and a set of training images of certain subjects as training sample matrix T, obtaining a sparse representation of the test sample and the training samples including an initial estimation of a sparse vector a, and constructing a new face dictionary comprising training samples with non-zero corresponding coefficients in the sparse vector a for the initial estimation. The method also includes obtaining new coefficients by solving a regularized least squares problem based on the constructed new face dictionary, and determining a face identity of the test sample based on minimum class residual calculated by using the new coefficients.

Abstract: A personalized channel recommendation method includes recording video programs of a plurality of channels, extracting image features of the recorded video programs of the plurality of channels, and storing the extracted image features of the recorded video programs of the plurality of channels in a feature database. The method also includes obtaining image feature data extracted from a video program being viewed by at least one user of a user device on a current channel, and detecting that the video program being watched by the user is an undesired program and determining a channel number of a channel of the video program being watched based on the image feature data and the feature database. Further, the method includes determining channel recommendation including at least one desired channel to be switched to from the undesired program, and generating an adaptive TV guide for the user to skip the undesired program and to switch to the desired channel.

Abstract: A face recognition method is provided to use sparse representation and regularized least squares-based classification on a computing device. The method includes obtaining an image to be recognized as a test sample y and a set of training images of certain subjects as training sample matrix T, obtaining a sparse representation of the test sample and the training samples including an initial estimation of a sparse vector a, and constructing a new face dictionary comprising training samples with non-zero corresponding coefficients in the sparse vector a for the initial estimation. The method also includes obtaining new coefficients by solving a regularized least squares problem based on the constructed new face dictionary, and determining a face identity of the test sample based on minimum class residual calculated by using the new coefficients.

Abstract: A method is provided for ranking media contents. The method includes receiving media contents through a network and extracting feature values of the received media contents. The method also includes implementing a parameter reinforcement learning process to obtain automatically distribution over relativeness and irrelativeness of the received media contents. Further, the method includes ranking the received media contents by a multi-armed bandit algorithm based on the obtained distribution over relativeness and irrelativeness of the received media contents.

Abstract: A method for video program recognition includes recording video sequences of each of a plurality of channels and obtaining a set of features of an image captured by a user device. The method also includes performing a camera registration operation on the video sequences to select video frames representing the recorded video sequences based on key video frames from a registered cameras associated with the recorded video sequences. Further, the method includes extracting features of at least some of the selected plurality of video frames of each channel, storing the extracted features, and comparing the obtained set of features with the stored features of each channel to determine whether there is a match between the obtained set of features of the image and features of a video frame from a specific channel and, when there is a match, determining the specific channel as a recognized channel number.

Abstract: A method for video sequential alignment is provided. The method includes inputting a first video sequence and a second video sequence, and extracting features of the first video sequence and the second video sequence. The method also includes generating an expanded path using a hierarchical sequential alignment algorithm and generating masks containing search space using keyframe matching. Further, the method includes calculating the lowest path cost within search space to find a minimal alignment path using a block A* algorithm and outputting a final alignment result after applying the block A* algorithm to search space.

Abstract: A method is provided for an intelligent video processing system based on object detection. The method includes receiving an input video sequence corresponding to a video program, obtaining a plurality of frames of the input video sequence, and obtaining a computational constraint and a temporal rate constraint. The method also includes determining one or more regions of interest (ROIs) of the plurality of frames based on the computational constraint and temporal rate constraint, and selecting a desired set of frames from the plurality of frames based on the ROIs such that the desired set of frames substantially represent a view path of the plurality of frames. Further, the method includes detecting object occurrences from the desired set of frames based on the selected desired set of frames such that a computational cost and a number of frames for detecting the object occurrences are under the computational constraint and temporal rate constraint.

Abstract: A video object retrieval method is provided. The method includes dividing an input video into a number of video shots and detecting target-class object occurrences and related-class object occurrences in each video shot. The method also includes pre-processing object bounding box images and performing automatic object extraction. Further, the method includes performing object-level feature extraction and integration and generating visual search results based on similarity scores.

Abstract: An automatic face annotation method is provided. The method includes dividing an input video into different sets of frames, extracting temporal and spatial information by employing camera take and shot boundary detection algorithms on the different sets of frames, and collecting weakly labeled data by crawling weakly labeled face images from social networks. The method also includes applying face detection together with an iterative refinement clustering algorithm to remove noise of the collected weakly labeled data, generating a labeled database containing refined labeled images, finding and labeling exact frames containing one or more face images in the input video matching any of the refined labeled images based on the labeled database, labeling remaining unlabeled face tracks in the input video by a semi-supervised learning algorithm to annotate the face images in the input video, and outputting the input video containing the annotated face images.

Abstract: An object identification method is provided. The method includes dividing an input video into a number of video shots, each containing one or more video frames. The method also includes detecting target-class object occurrences and related-class object occurrences in each video shot. Further, the method includes generating hint information including a small subset of frames representing the input video and performing object tracking and recognition based on the hint information. The method also includes fusing tracking and recognition results and outputting labeled objects based on the combined tracking and recognition results.

Abstract: A method is provided for enhanced video services to at least one user. The method includes receiving a first input video, and processing the received input video to obtain multi-dimensional information associated with the input video. The method also includes obtaining user recommendation information based on user behavior, and creating enhanced video information based on the multi-dimensional information associated with the input video and the user recommendation information. Further, the method includes associating the enhanced video information with the first input video to create an enhanced video of the first input video, and playing back the enhanced video to the user.

Abstract: A method for video sequential alignment is provided. The method includes inputting a first video sequence and a second video sequence, and extracting features of the first video sequence and the second video sequence. The method also includes generating an expanded path using a hierarchical sequential alignment algorithm and generating masks containing search space using keyframe matching. Further, the method includes calculating the lowest path cost within search space to find a minimal alignment path using a block A* algorithm and outputting a final alignment result after applying the block A* algorithm to search space.

Abstract: A method is provided for content alignment. The method includes obtaining a first content sequence and a second content sequence different from the first content sequence. The method also includes representing each of the first content sequence and the second content sequence in a hierarchical structure containing an ordered root element sequence and a sub-tree structure. The ordered root element sequence includes a plurality of root elements and each root element is associated with a sub-tree of elements. The method also includes determining a desired alignment between the first content sequence and the second content sequence using dynamic programming, and outputting results of the desired alignment between the first content sequence and the second content sequence.

Abstract: A method for a mobile platform containing a mobile terminal having an operating system includes initializing a plurality of user environments (UEs) on the mobile terminal over the operating system, including a current UE running on the mobile terminal. The plurality of UEs are capable of being switched among one another based on one or more of predetermined conditions without changing the operating system. The method also includes collecting sensing data on certain parameters associated with operation of the mobile terminal, and processing the sensing data to indicate at least one of the predetermined conditions. Further, the method includes determining whether the current UE suits the at least one of the predetermined conditions indicated by processing the sensing data and, when the current UE does not suit the condition of the mobile terminal, switching the current UE to a desired UE from the plurality of UEs.

Abstract: Methods and systems for using a video data compression algorithm with parallel processing capability are provided. AC and DC coefficients associated with blocks of the video data, along with quantization errors, may be encoded using a variable length code. The quantization errors may be encoded using a scheme that assigns priorities to the quantization errors based on the position of their associated AC and/or DC coefficients in a block of the video data. The quantization errors may be appended to a bitstream in an order based on these priorities that enables parallel coding of the quantization errors and AC and DC coefficients in each block of video data. Data packing schemes may also be applied to the coded data to maximize the use of bandwidth resources in encoding and/or decoding.

Abstract: An object identification method is provided. The method includes dividing an input video into a number of video shots, each containing one or more video frames. The method also includes detecting target-class object occurrences and related-class object occurrences in each video shot. Further, the method includes generating hint information including a small subset of frames representing the input video and performing object tracking and recognition based on the hint information. The method also includes fusing tracking and recognition results and outputting labeled objects based on the combined tracking and recognition results.