Abstract: In one aspect, the present disclosure describes a process for automatic artifact compensation in a digital representation of an image. The process includes detecting, by a processor, regions corresponding to facial images within the digital representation; locating, by the processor, red-eye regions within the detected regions; and automatically modifying, by the processor, the located red-eye regions to provide a modified image.

Abstract: A “music video parser” automatically detects and segments music videos in a combined audio-video media stream. Automatic detection and segmentation is achieved by integrating shot boundary detection, video text detection and audio analysis to automatically detect temporal boundaries of each music video in the media stream. In one embodiment, song identification information, such as, for example, a song name, artist name, album name, etc., is automatically extracted from the media stream using video optical character recognition (OCR). This information is then used in alternate embodiments for cataloging, indexing and selecting particular music videos, and in maintaining statistics such as the times particular music videos were played, and the number of times each music video was played.

Abstract: Systems and methods for learning-based automatic commercial content detection are described. In one aspect, program data is divided into multiple segments. The segments are analyzed to determine visual, audio, and context-based feature sets that differentiate commercial content from non-commercial content. The context-based features are a function of single-side left and/or right neighborhoods of segments of the multiple segments.

Abstract: A technique is provided for organizing and displaying digital photographs based on time. The technique includes inputting data representing a photograph and storing the data as a photograph image file. The technique then identifies the manner in which the photograph image file stores time information (such as date and time of day). For instance, the technique determines whether the time information is digitally encoded in the image file, or whether it is embedded within the image data itself. The technique next includes extracting the time information from the photograph image file using a technique appropriate to the identified manner in which the time information is stored, to produce extracted time information. The photographs are then inserted into a time sequence based on the extracted time information, and presented on a calendar display at a location representative of the chronological placement of the photograph within the time sequence.

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 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: 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: 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: A method and apparatus are provided for augmenting a language model with a class entity dictionary based on corrections made by a user. Under the method and apparatus, a user corrects an output that is based in part on the language model by replacing an output segment with a correct segment. The correct segment is added to a class of segments in the class entity dictionary and a probability of the correct segment given the class is estimated based on an n-gram probability associated with the output segment and an n-gram probability associated with the class. This estimated probability is then used to generate further outputs.

Abstract: A method and apparatus are provided for adapting a language model to a task-specific domain. Under the method and apparatus, the relative frequency of n-grams in a small training set (i.e. task-specific training data set) and the relative frequency of n-grams in a large training set (i.e. out-of-domain training data set) are used to weight a distribution count of n-grams in the large training set. The weighted distributions are then used to form a modified language model by identifying probabilities for n-grams from the weighted distributions.

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.

Abstract: A language system facilitates entry of an input string into a mobile device (e.g., cellular phones, PDAs, pagers, etc.) using discrete keys on a keypad, such as a 10-key keypad. The keys have associated letters of an alphabet (e.g., an English alphabet). The key input is representative of one or more phonetic characters (e.g., Chinese Pinyin). Based on this input string, the language system derives the most likely corresponding language characters (e.g., Chinese Hanzi) intended by the user. The language system uses multiple different search engines and language models to aid in deriving the most probable characters. When the language system recognizes possible language characters, the mobile device displays the possible language characters for user selection. The available choices are indexed by specifically chosen selection keys that represent letters of the alphabet that do not commonly follow the phonetic characters already entered.