The first step in the analysis of video content is the partitioning of a long video sequence into short homogeneous temporal segments. The homogeneity property ensures that the segments are taken by a single camera and represent a continuous action in time and space. These segments will then be used as atomic temporal components for higher level analysis like browsing, classification, indexing and retrieval. The novelty of our approach is to use color information to cut down the video into segments dynamically homogeneous using a criterion inspired by compact coding theory. First, we use a statistical detection framework to detect abrupt "shot" transitions (strong discontinuities in the data stream), then, we perform an information-based segmentation inside each "shot" using a Minimum Message Length (MML) criterion and minimization by a Dynamic Programming Algorithm (DPA). We show

Abstract — New methods for detecting shot boundaries in video sequences and for extracting key frames using metrics based on information theory are proposed. The method for shot boundary detection relies on the mutual information (MI) and the joint entropy (JE) between the frames. It can detect cuts, fadeins and fade-outs. The detection technique was tested on the TRECVID2003 video test set having different types of shots and containing significant object and camera motion inside the shots. It is demonstrated that the method detects both fades and abrupt cuts with high accuracy. The information theory measure provides us with better results because it exploits the inter-frame information in a more compact way than frame subtraction. It was also successfully compared to other methods published in literature. The method for key frame extraction uses mutual information as well. We show that it captures satisfactorily the visual content of the shot. Index Terms — shot boundary detection, entropy, mutual information, detection accuracy, video segmentation, video analysis, key frame extraction. I.

This study surveys current trends/methods in video retrieval. The major themes covered by the study include shot segmentation, key frame extraction, feature extraction, clustering, indexing and video retrieval-by similarity, probabilistic, transformational, refinement and relevance feedback. This work has done in an aim to assist the upcoming researchers in the field of video retrieval, to know about the techniques and methods available for video retrieval.

Multimedia applications have been extended over the past decade, and efficient methods for video indexing and retrieval are required to confront this situation. Video temporal segmentation is the first and essential step for visual data processing, which aims to find the location of shot boundaries, including abrupt shot boundaries (AB) and gradual shot boundaries (GB). In this paper we propose an algorithm for video shot detection with Frame-Skipping technique. In our algorithm only some typical frames are selected for initial processing, which can be obtained by Frame-Skipping Module (FSM). As a visual description, we may choose 1 or 2 frames from 10 consecutive frames. To diminish the error caused by Frame-Skipping technique, we use Abrupt-Boundary Module (ABM) and Gradual-Boundary Module (GBM) to detect video shots, where the ABM based on global histogram comparison is used to detect abrupt shot boundaries and the GBM based on local histogram comparison concentrates on detecting gradual transitions. This new algorithm can reduce about 40%∼70 % time-consumption compared with some previous algorithms on 4 typical video clips (movie, cartoon, sports and news).

Detection of scene changes is an elementary step in automatic video processing like indexing, segmentation or transcoding. Video indexing and segmentation allow fast browsing without decoding the complete video. In the case of transcoding, the information about scene changes such as cuts and fades as well as about special movements like rotations or zooms in video frames is helpful to determine suitable transcoding parameters. In the compressed domain only information about DCT values as well as motion information can be used to determine such scene changes and movements. Therefore we defined different measures which use the encoded DCT values and motion vectors of each compressed frame. Based on these measures as well as on motion vector histograms we present a fast approach to detect different kinds of scene changes and special movements in MPEG-4 videos in the compressed domain. 1.

Abstract — A new method for detecting shot boundaries in video sequences by fusing features obtained by singular value decomposition (SVD) and mutual information (MI) is proposed. The first method relies on performing singular value decomposition on a matrix created from 3D color histograms of single frames. The method can detect cuts and gradual transitions, such as dissolves, fades and wipes. The second method relies on evaluating mutual information between two consecutive frames. It can detect abrupt cuts, fade-ins and fade-outs with very high accuracy. Combination of features derived from these methods and subsequent processing through a clustering procedure results in very efficient detection of abrupt cuts and gradual transitions, as demonstrated by experiments on TRECVID2004 video test set containing different types of shots with significant object and camera motion inside the shots. I.