Increasingly recognized in biological psychiatry, rodent self-grooming is a complex patterned behavior with evolutionarily conserved cephalo-caudal progression. While grooming is traditionally assessed by the latency, frequency and duration, its sequencing represents another important domain sensitive to various experimental manipulations. Such behavioral complexity requires novel objective approaches to quantify rodent grooming, in addition to time-consuming and highly variable manual observation. The present study combined modern behavior-recognition video-tracking technologies (CleverSys, Inc.) with manual observation to characterize in-depth spontaneous (novelty-induced) and artificial (water-induced) self-grooming in adult male C57BL/6J mice. We specifically focused on individual episodes of grooming (paw licking, head washing, body/leg washing, and tail/genital grooming), their duration and transitions between episodes. Overall, the frequency, duration and transitions detected using the automated approach significantly correlated with manual observations (R=0.51-0.7, p<0.001-0.05). This data validates the software-based detection of grooming, also indicating that behavior-recognition tools can be applied to characterize both the amount and sequential organization (patterning) of rodent grooming. Together with further refinement and methodological advancement, this approach will foster high-throughput neurophenotyping of grooming, with multiple applications in drug screening and testing of genetically modified animals.

Animal self‐grooming behavior is becoming increasingly recognized in neurophenotyping research. Rodent grooming and its complex sequencing are sensitive to various genetic and pharmacological manipulations. However, its phenotyping is usually limited to global endpoints such as frequency of bouts and their duration. Here, we used custom‐upgraded HomeCageScan video‐tracking software (Clever Sys. Inc., Reston, VA) to record grooming behavior of adult male C57BL/6J mice in transparent observation cylinders for 5 min. This allowed us not only to perform behavioral quantification of specific grooming patterns (such as paw licking and body/leg grooming) but also analyze their transitions, revealing significant correlations (P<0.0005–0.02, R=0.51–0.70) with manual observations for total number and selected specific grooming transitions. Our data suggests that novel, high‐throughput automated neurophenotyping of grooming behavior can be developed for biomedical research based on this approach.

Automated classification of digital video is emerging as an important piece of the puzzle in the design of content management systems for digital libraries. The ability to classify videos into various classes such as sports, news, movies, or documentaries, increases the efficiency of indexing, browsing, and retrieval of video in large databases. In this paper, we discuss the extraction of features that enable identification of sports videos directly from the compressed domain of MPEG video. These features include detecting the presence of action replays, determining the amount of scene text in vide, and calculating various statistics on camera and/or object motion. The features are derived from the macroblock, motion,and bit-rate information that is readily accessible from MPEG video with very minimal decoding, leading to substantial gains in processing speeds. Full-decoding of selective frames is required only for text analysis. A decision tree classifier built using these features is able to identify sports clips with an accuracy of about 93 percent.

Automated classification of digital video is emerging as an important piece of the puzzle in the design of content management systems for digital libraries. The ability to classify videos into various genres such as sports, news, movies, or documentaries increases the efficiency of indexing, browsing, and retrieval of video in large databases. In this paper, we present an automated technique for identifying slow-motion replays directly from the compressed domain of MPEG video. It uses the macroblock, motion, and bit-rate information that is readily accessible from MPEG video with very minimal decoding, leading to enormous gains in processing speeds.

Our research concentrates on developing a novel HDTV content management system that enables end users to search, retrieve and browse archived standard definition (SD) and high definition (HD) television material for program production and content repurposing in a digital television studio. We have developed the first system that automatically analyzes motion occurring in MPEG-2 coded SD and HD videos within the compressed domain itself and produces descriptors characterizing the global visual motion in videos, for content-based search and retrieval applications. We describe our robust and efficient scheme for automatically generating a flow characterization of a video bitstream without decompression, which is a frame-type-independent uniform motion representation amenable for consistent interpretation and computed from the raw motion vectors encoded in the MPEG-2 bitstreams. We propose novel techniques to handle all the different prediction schemes that are employed with different frame types and picture structures of MPEG-2 during the motion compensation process to deal with interlaced and progressive modes. Experiments with thousands of frames from SD and HD video streams demonstrate the accuracy of our flow estimation process and the effectiveness of utilizing flow vectors for annotation of perceived global motion in video streams.

Efficient content-based search and retrieval of video databases has become an important industrial and consumer application due to rapid proliferation of compressed digital video data on the Internet and corporate Intranets, and due to the launch of high definition television (HDTV) broadcasts in 1998. We have developed the first HDTV video content management system that automatically analyzes motion occurring in MPEG-2 encoded videos within the compressed domain itself. Our system produces descriptors characterizing the global motion in videos for content retrieval and repurposing applications in a digital television studio. These motion-direction and magnitude based video labels can be directly, incorporated as annotation indexes into a database for querying, or used to construct higher level event descriptions of videos. Results from our ongoing experiments with tens of thousands of frames obtained from several MPEG-1,2 video streams of various genres demonstrate the good performance of our annotation system in terms of motion identification accuracy and computational efficiency

Video segmentation plays an integral role in many multimedia applications, such as digital libraries, content management systems, and various other video browsing, indexing, and retrieval systems. Many algorithms for segmentation of video have appeared within the past few years. Most of these algorithms perform well on cuts, but yield poor performance on gradual transitions or special effects edits. A complete video segmentation system must also achieve good performance on special effect edit detection. In this paper, we discuss the performance of our Video Trails-based algorithms, with other existing special effect edit-detection algorithms within the literature. Results from experiments testing for the ability to detect edits from TV programs, ranging from commercials to news magazine programs, including diverse special effect edits, which we have introduced.

A video sequence can be represented as a trajectory curve in a high dimensional feature space. This video curve can be analyzed by tools similar to those developed for planar curves. In particular, the classic binary curve splitting algorithm has been found to be a useful tool for video analysis. With a splitting condition that checks the dimensionality of the curve segment being split, the video curve can be recursively simpliied and represented as a tree structure, and the frames that are found to be junctions between curve segments at diierent levels of the tree can be used as keyframes to summarize the video sequences at diierent levels of detail. These keyframes can be combined in various spatial and temporal conngurations for browsing purposes. We describe a simple video player that displays the keyframes sequentially and lets the user change the summarization level on the y with a slider. We also describe an approach to automatically selecting a summarization level that provides a concise and representative set of keyframes.

To keep pace with the increased popularity of digital video as an archival medium, the development of techniques for fast and efficient analysis of video streams is essential. In particular, solutions to the problems of storing, indexing, browsing, and retrieving video data from large multimedia databases are necessary to allow access to these collections. Given that video is often stored efficiently in a compressed format, the costly overhead of decompression can be reduced by analyzing the compressed representation directly. In earlier work, we presented compressed domain parsing techniques which identified shots, subshots, and scenes. In this article, we present efficient key frame selection, feature extraction, indexing, and retrieval techniques that are directly applicable to MPEG compressed video. We develop a frame type independent representation which normalizes spatial and temporal features including frame type, frame size, macroblock encoding, and motion compensation vectors. Features for indexing are derived directly from this representation and mapped to a low-dimensional space where they can be accessed using standard database techniques. Spatial information is used as primary index into the database and temporal information is used to rank retrieved clips and enhance the robustness of the system. The techniques presented enable efficient indexing, querying, and retrieval of compressed video as demonstrated by our system which typically takes a fraction of a second to retrieve similar video scenes from a database, with over 95% recall (C) 1998 SPIE and IS&T. [S1017-9909(98)00302-X].

Article Video summarization by curve simplification Share on Authors: Daniel DeMenthon Language and Media Processing (LAMP), University of Maryland, College Park, MD Language and Media Processing (LAMP), University of Maryland, College Park, MDView Profile , Vikrant Kobla Language and Media Processing (LAMP), University of Maryland, College Park, MD Language and Media Processing (LAMP), University of Maryland, College Park, MDView Profile , David Doermann Language and Media Processing (LAMP), University of Maryland, College Park, MD Language and Media Processing (LAMP), University of Maryland, College Park, MDView Profile Authors Info & Claims MULTIMEDIA '98: Proceedings of the sixth ACM international conference on MultimediaSeptember 1998 Pages 211–218https://doi.org/10.1145/290747.290773Published:01 September 1998 145citation1,157DownloadsMetricsTotal Citations145Total Downloads1,157Last 12 Months53Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Development of various multimedia applications is dependent on the availability of fast and efficient storage, browsing, indexing, and retrieval techniques. Given that video is stored efficiently in a compressed format, the costly overhead of decompression can be avoided by analyzing the compressed representation directly. We describe techniques that can be used to extract viable features for indexing shots of video directly from the compressed domain. We develop a type independent representation of frames present in an MPEG video and show how it can be used directly for indexing