Abstract: A content retrieval method including: extracting a plurality of fingerprints including a plurality of video fingerprints and audio fingerprints from contents stored in a content database; determining representative video fingerprints of the video frames and representative audio fingerprints of the audio sequences; determining a data rate indicating a storage limitation and a coverage indicating a number of searching results to be returned; storing selected representative video fingerprints and representative audio fingerprints based on the storage limitation indicated by the data rate in a fingerprint database; receiving a query containing at least one of video data and audio data submitted by a user; extracting at least one query fingerprint representing the query; determining a number of fingerprints most matching the query fingerprint based on the coverage to generate search results indicating matching contents represented by the number of most matching fingerprints; and returning the search results to the

Abstract: The present invention provides a face recognition method on a computing device, comprising: storing a plurality of training face images, each training face image corresponding to a face class; obtaining one or more face test samples; applying a representation model to represent the face test sample as a combination of the training face images and error terms, wherein a coefficient vector is corresponded to the training face images; estimating the coefficient vector and the error terms by solving a constrained optimization problem; computing a residual error for each face class, the residual error for a face class being an error between the face test sample and the face test sample's representation model represented by the training samples in the face class; classifying the face test sample by selecting the face class that yields the minimal residual error; and presenting the face class of the face test sample.

Abstract: A method is provided for recommending a desired func sequence to a user. The method includes obtaining a user intention list including at least one user intention; separating the user intention into a plurality of tasks; and creating a task flow graph for the plurality of tasks based on user usage data. Each vertex in the task flow graph represents a task and indicating an importance of the task. The method also includes creating a func flow graph based on the user usage data and temporal sequences of the tasks and funcs, and each vertex in the func flow graph represents a func and indicating an importance of the func. Further, the method includes determining a desired func sequence to fulfil the user intention based on the user usage data, the task flow graph, and the func flow graph; and recommending the desired func sequence to the user.

Abstract: A method for multimodal clue based personalized app function recommendation is provided. The method includes receiving an app search query from a user, obtaining a plurality of real-time clues collected from the user and extracting a plurality of features to represent the collected clues, wherein the plurality of features include structured features and text features. The method also includes generating a joint representation of the multimodal clues based on the plurality of extracted features and creating a logistic regression model based on the joint representation of the multimodal clues. Further, the method includes ranking a list of app functions using the logistic regression model and displaying the ranked app function list for the user.

Abstract: A unified attractiveness prediction method is provided. The method includes receiving a plurality of videos and extracting at least one of metadata and view data from each of the plurality of received videos, wherein the metadata is information for describing video contents, and the view data is a total number of users who watch the video. The method also includes obtaining potential view amounts of the plurality of received videos and calculating impact factor scores of the plurality of received videos based on the potential view amounts, each impact factor score including a numerical score for indicating a degree of effectiveness of a corresponding video. Further, the method includes providing a video with a highest impact factor score based on the calculated impact factor scores.

Abstract: A monoscopic low-power mobile device is capable of creating real-time images and videos for multiple viewpoints from a single captured view. The device uses statistics from an autofocusing process to create a block depth map of a single capture view. Artifacts in the block depth map are reduced and an image depth map is created. Alternate views are created from the image depth map using a Z-buffer based surface recover process and a disparity map which is a function of a geometric vision model.

Abstract: An inter-app switching method on a mobile device is provided. The method includes receiving at least one input associated with mobile app usage from a user and determining whether a pre-defined trigger is enabled based on the at least one received input. The method also includes performing routine user operations on the mobile device when the pre-defined trigger is disabled. Further, the method includes discovering a current user intent by utilizing at least one user interaction, recommending at least one mobile app recommendation and access point including at least an entrance to a function and a type of the function (FUNC) to the user, receiving a selection by the user from the at least one recommended FUNC, and directly switching from a current app page to another app page without returning to any home screen on the mobile device when the pre-defined trigger is enabled.

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 video retrieval method is provided. The method includes dividing video sequences stored in a video database into a plurality of video frames, extracting by a predetermined feature extracting algorithm a plurality of selected fingerprints from the plurality of video frames representing features having characteristic information specific to the plurality of video frames, and storing the plurality of extracted selected fingerprints in a fingerprint database. The method also includes receiving a query video sequence submitted by a user, extracting by the predetermined feature extracting algorithm at least one query fingerprint representing at least one query frame from the received query video sequence, and calculating similarity between the query fingerprint and the selected fingerprints from the fingerprint database to search for a match for the query fingerprint.

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: 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 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: The present invention provides a face recognition method. The method includes obtaining a plurality of training face images which belongs to a plurality of face classes and obtaining a plurality of training dictionaries corresponding to the training face images. A face class includes one or more training face images. The training dictionaries include a plurality of deep feature matrices. The method further includes obtaining an input face image. The input face image is partitioned into a plurality of blocks, whose corresponding deep feature vectors are extracted using a deep learning network. A collaborative representation model is applied to represent the deep feature vectors with the training dictionaries and representation vectors. A summation of errors for all blocks corresponding to a face class is computed as a residual error for the face class. The input face image is classified by selecting the face class that yields a minimum residual error.

Abstract: The present invention provides an information presentation system, comprising: a video module configured to obtain video content to be presented to a user; a network module configured to obtain certain information related to the video content including at least a first type of related information, a second type of related information, and a third type of related information; a first display configured to display the video content at a primary location in front of the user; one or more second displays configured to display the first type related information at the primary location outside the first display and to display the second type related information on a secondary location along the side of the primary location; an interactive display configured to interact with the user and display the third type related information; and a system controller configured to perform content assignments to the displays according to requirements from the user.

Abstract: A method for personalized direct app transition on a mobile device is provided. The method includes receiving behavior statistics data of a user of the mobile device and analyzing behavior pattern and preference of the user based on the received behavior statistics data. The method also includes determining at least one mobile app recommendation and access point including at least an entrance to the function and a type of the function (FUNC) based on the behavior pattern and preference of the user on the mobile device, where the at least one FUNC includes at least a second app different from the first app and recommending the at least one FUNC to the user. Further, the method includes receiving a selection of the second app by the user from the recommended FUNC and directly transitioning from a first app page to a second app page without returning to any home screen.

Abstract: The present invention provides a face recognition method on a computing device, comprising: storing a plurality of training face images, each training face image corresponding to a face class; obtaining one or more face test samples; applying a representation model to represent the face test sample as a combination of the training face images and error terms, wherein a coefficient vector is corresponded to the training face images; estimating the coefficient vector and the error terms by solving a constrained optimization problem; computing a residual error for each face class, the residual error for a face class being an error between the face test sample and the face test sample's representation model represented by the training samples in the face class; classifying the face test sample by selecting the face class that yields the minimal residual error; and presenting the face class of the face test sample.

Abstract: A method for scalable user intent mining is provided. The method includes detecting named entities from a plurality of query logs in a public query log dataset and generating features of the plurality of query logs based on the detected named entities. The method also includes applying a multimodal restricted boltzmann machine (RBM) on the generated features of the plurality of query logs to train a public multimodal RBM and generating a plurality of public query representations. Further, the method includes receiving a search query from a user, determining whether there are a plurality of history queries of the user. When there is no history query, user intent is predicted using the public multimodal RBM. When there are the history queries, the public multimodal RBM is applied on the plurality of history queries to train a personalized multimodal RBM, and the user intent is predicted using the personalized multimodal RBM.

Abstract: A method for sharing information among multiple devices is provided. The method includes a sensing device sensing signals of at least one object associated with a targeting device and extracting at least one feature of the object from the sensed signals. The method also includes the sensing device broadcasting the extracted feature of the object on a determined network containing a plurality of targeting devices and receiving feedbacks from the plurality of targeting devices on the network in response to the broadcasting. Further, the method includes the sensing device automatically identifying one of the plurality of targeting devices based on the received feedbacks, synchronizing information with the identified targeting device and displaying the information to a user of the sensing device.

Abstract: A method for balancing recognition accuracy and power consumption is provided. The method includes activating a plurality of sensors onboard a device for a first time period and calculating sensor readings by using a calculation function. The method also includes determining a plurality of sensor subsets of the plurality of sensors and calculating corresponding errors for the plurality of sensor subsets for the first time period. Further, the method includes calculating respective power consumption of running the sensor subsets of the plurality of sensors from the plurality of sensor subsets that produce the error below the user-defined error threshold and selecting a sensor subset with minimum power consumption from the sensor subsets as an optimal sensor subset. In addition, the method includes deactivating the plurality of sensors not in the optimal sensor subset and calculating and displaying sensor readings with the optimal sensor subset for a second time period.

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.