Koninklijke Philips Corporation N.v.

Philips Electronics North America Corporation

U.S. Philips Corporation

Jan Hermanus Elenbaas

Nevenka Dimitrova

Thomas F McGee

Lalitha Agnihotri

Jacquelyn Annette Martino

Radu S Jasinschi

Serhan Dagtas

David Berkowitz

Aaron S Mendelsohn

Job Rutgers

Thomas Mc Gee

Eugene Leyvi

Carolyn Ramsey

A method of selecting, storing and delivering desired audio/data/visual information includes the steps of determining viewing preferences of a viewer and receiving a first group of audio/data/visual signals, for example, broadcast and cable television signals or internet-based signals. Based on the first group of audio/data/visual signals, a second group of audio/data/visual signals, which is a subset of the first group of audio/data/visual signals, is identified. The second group of audio/data/visual signals is selected based on the association of EPG data for each signal with the viewing preferences of the viewer. Content data is then extracted from the second group of audio/data/visual signals and compared with the viewing preferences. The content data may include, for example, closed-captioned text, EPG data, audio information, visual information and transcript information. Based on the comparison of the content data extracted from the second group of audio/data/visual signals with the viewing preferences, audio/data/visual information contained in the second group of audio/data/visual signals which is of interest to the viewer is identified and stored for review at the viewers convenience.

Method and apparatus for audio/data/visual information

There is disclosed, for use in video text analysis system, a video processing device for searching video streams for one or more user-selected image text attributes. The video processing device comprises an image processor capable detecting and extracting image text from video frames, determining attributes of the extracted image text, comparing the extracted image text attributes and the user-selected image text attributes, and, if a match occurs, modifying, transferring, and/or labeling at least a portion of the video stream in accordance with user commands. The invention uses the user-selected image text attributes to search through an archive of video clips to 1) locate particular types of events, such as news programs or sports events; 2) locate programs featuring particular persons or groups; 3) locate programs by name; 4) save or remove all or some commercials, and to otherwise sort, edit, and save all of, or portions of, video clips according to image text that appears in the frames of the video clips.

Method and system for analyzing video content using detected text in video frames

In order to characterize video content, key frames are identified in that content. The key frames are collapsed into histograms. The histograms are grouped into families. The families are each represented by a family histogram. The family histograms characterize the video content. The family histograms can be used to determine program boundaries or index the video content.

Histogram method for characterizing video content

A video indexing system analyzes contents of source video and develops a visual table of contents using selected images. The source video is analyzed to detect video cuts from one scene to another, and static scenes. Keyframes are selected for each significant scene. A keyframe filtering process filters out less desired frames including, for example, unicolor frames, or those frames having a same object as a primary focus or one primary focuses. A visual index is created from those frames remaining after the keyframe filtering and stored for retrieval. The visual index may be retrieved by a user who may then display the visual index on a display. The user may select one of the frames displayed in the visual index and the source video may be manually (by the user) or automatically advanced to that frame of the source video. Additionally, a user may print the visual index.

Visual indexing system

A video indexing system analyzes contents of source video and develops a visual table of contents using selected images. A system for detecting significant scenes detects video cuts from one scene to another, and static scenes based on DCT coefficients and macroblocks. A keyframe filtering process filters out less desired frames including, for example, unicolor frames, or those frames having a same object as a primary focus or one primary focuses. Commercials may also be detected and frames of commercials eliminated.

Significant scene detection and frame filtering for a visual indexing

A method and apparatus for forming a visual index of scenes in a video image which has been or is being recorded in a computer readable memory. A selected number of keyframes are derived from the recorded image, each being representative of a respective scene therein. The keyframes are then ordered into a selected number of levels of detail of the scenes represented thereby, each level including a predetermined number of keyframes, each subsequent level including keyframes of greater detail than those in a preceding level. A header file is then formed which is descriptive of the ordered set of keyframes, and the header file is stored together with the ordered set of keyframes in the computer readable memory. A user can thereby identify and obtain optimized retrieval in accordance with his preferences of particular segments of the video image from a relatively slow memory device. The method and apparatus are equally applicable to formation of an indexed order of binary large objects ('blobs') in a set of multimedia documents in accordance with a user's preferences.

Apparatus and method for optimizing keyframe and blob retrieval and

A commercial detection apparatus includes a frame grab thread which acquires frames from an input data stream to be analyzed by a cut detector thread and a filter thread. A commercial detection thread determines whether a commercial has occurred from data created by the cut detector thread and the filter thread stored in a memory. A factor used by the commercial detection thread is whether a black frame has occurred. The input data stream is divided into a plurality of regions and then a maximum and minimum value for a section of these regions is determined and compared to one another. If the maximum and minimum values are close enough, and the maximum value is below a threshold, then the frame is deemed to be black. The commercial detection thread also looks at the average cut frame distance, cut rate, changes in the average cut frame distance, the absence of a logo, a commercial signature detection, brand name detection, a series of black frames preceding a high cut rate, similar frames located within a specified period of time before a frame being analyzed and character detection. During playback, the detected commercials are either skipped or substituted with alternate content.

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New York, NY, United States of America

Jan Hermanus Elenbaas