John Boreczky

Bio: John Boreczky is an academic researcher from Fuji Xerox. The author has contributed to research in topics: Video tracking & Video capture. The author has an hindex of 27, co-authored 58 publications receiving 4229 citations. Previous affiliations of John Boreczky include FX Palo Alto Laboratory & Xerox.

Comparison of video shot boundary detection techniques

Abstract: Many algorithms have been proposed for detecting video shot boundaries and classifying shot and shot transition types. Few published studies compare available algorithms, and those that do have looked at limited range of test material. This paper presents a comparison of several shot boundary detection and classification techniques and their variations including histograms, discrete cosine transform, motion vector, and block matching methods. The perfor- mance and ease of selecting good thresholds for these algorithms are evaluated based on a wide variety of video sequences with a good mix of transition types. Threshold selection requires a trade-off between recall and precision that must be guided by the target application. © 1996 SPIE and IS&T.

Video Manga: generating semantically meaningful video summaries

Abstract: This paper presents methods for automatically creating pictorial video summaries that resemble comic books. The relative importance of video segments is computed from their length and novelty. Image and audio analysis is used to automatically detect and emphasize meaningful events. Based on this importance measure, we choose relevant keyframes. Selected keyframes are sized by importance, and then efficiently packed into a pictorial summary. We present a quantitative measure of how well a summary captures the salient events in a video, and show how it can be used to improve our summaries. The result is a compact and visually pleasing summary that captures semantically important events, and is suitable for printing or Web access. Such a summary can be further enhanced by including text captions derived from OCR or other methods. We describe how the automatically generated summaries are used to simplify access to a large collection of videos.

Comparison of video shot boundary detection techniques

Abstract: Many algorithms have been proposed for detecting video shot boundaries and classifying shot and shot transition types. Few published studies compare available algorithms, and those that do have looked at limited range of test material. This paper presents a comparison of several shot boundary detection and classification techniques and their variations including histograms, discrete cosine transform, motion vector, and block matching methods. The performance and ease of selecting good thresholds for these algorithms are evaluated based on a wide variety of video sequences with a good mix of transition types. Threshold selection requires a trade-off between recall and precision that must be guided by the target application.© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

A hidden Markov model framework for video segmentation using audio and image features

Abstract: This paper describes a technique for segmenting video using hidden Markov models (HMM). Video is segmented into regions defined by shots, shot boundaries, and camera movement within shots. Features for segmentation include an image-based distance between adjacent video frames, an audio distance based on the acoustic difference in intervals just before and after the frames, and an estimate of motion between the two frames. Typical video segmentation algorithms classify shot boundaries by computing an image-based distance between adjacent frames and comparing this distance to fixed, manually determined thresholds. Motion and audio information is used separately. In contrast, our segmentation technique allows features to be combined within the HMM framework. Further, thresholds are not required since automatically trained HMMs take their place. This algorithm has been tested on a video data base, and has been shown to improve the accuracy of video segmentation over standard threshold-based systems.

Media browser using multimodal analysis

Abstract: A media browser, graphical user interface and method for browsing a media file wherein a user selects at least one feature in a media file and is provided with information regarding the existence of the selected feature in the media file. Based on the information, the user can identify and playback portions of interest in a media file. Features in a media file, such as a speaker's identity, applause, silence, motion, or video cuts, are preferably automatically time-wise evaluated in the media file using known methods. Metadata generated based on the time-wise feature evaluation are preferably mapped to confidence score values that represent a probability of a corresponding feature's existence in the media file. Confidence score information is preferably presented graphically to a user as part of a graphical user interface, and is used to interactively browse the media file.

Multimedia in Learning

Abstract: A wide variety of media can be used in learning, including distance learning, such as print, lectures, conference sections, tutors, pictures, video, sound, and computers. Any one instance of distance learning will make choices among these media, perhaps using several.

Audio and video program recording, editing and playback systems using metadata

Abstract: A system for utilizing metadata created either at a central location for shared use by connected users, or at each individual user's location, to enhance user's enjoyment of available broadcast programming content. A variety of mechanisms are employed for automatically and manually identifying and designating programming segments, associating descriptive metadata which the identified segments, distributing the metadata for use at client locations, and using the supplied metadata to selectively record and playback desired programming.

Content-based multimedia information retrieval: State of the art and challenges

Abstract: Extending beyond the boundaries of science, art, and culture, content-based multimedia information retrieval provides new paradigms and methods for searching through the myriad variety of media all over the world. This survey reviews 100p recent articles on content-based multimedia information retrieval and discusses their role in current research directions which include browsing and search paradigms, user studies, affective computing, learning, semantic queries, new features and media types, high performance indexing, and evaluation techniques. Based on the current state of the art, we discuss the major challenges for the future.

Display screen or portion thereof with graphical user interface

Abstract: Newness and distinctiveness is claimed in the features of ornamentation as shown inside the broken line circle in the accompanying representation.

Computer system and process for transferring multiple high bandwidth streams of data between multiple storage units and multiple applications in a scalable and reliable manner

Abstract: Multiple applications request data from multiple storage units over a computer network. The data is divided into segments and each segment is distributed randomly on one of several storage units, independent of the storage units on which other segments of the media data are stored. At least one additional copy of each segment also is distributed randomly over the storage units, such that each segment is stored on at least two storage units. This random distribution of multiple copies of segments of data improves both scalability and reliability. When an application requests a selected segment of data, the request is processed by the storage unit with the shortest queue of requests. Random fluctuations in the load applied by multiple applications on multiple storage units are balanced nearly equally over all of the storage units. This combination of techniques results in a system which can transfer multiple, independent high-bandwidth streams of data in a scalable manner in both directions between multiple applications and multiple storage units.