Adaptive Edge-Oriented Shot Boundary Detection

We study the problem of video shot boundary detection using an adaptive edge-oriented framework. Our approach is distinct in its use of multiple multilevel features in the required processing. Adaptation is provided by a careful analysis of these multilevel features, based on shot variability. We consider three levels of adaptation: at the feature extraction stage using locally-adaptive edge maps, at the video sequence level, and at the individual shot level. We show how to provide adaptive parameters for the multilevel edge-based approach, and how to determine adaptive thresholds for the shot boundaries based on the characteristics of the particular shot being indexed. The result is a fast adaptive scheme that provides a slightly better performance in terms of robustness, and a five fold efficiency improvement in shot characterization and classification. The reported work has applications beyond direct video indexing, and could be used in real-time applications, such as in dynamic monitoring and modeling of video data traffic in multimedia communications, and in real-time video surveillance. Experimental results are included

Shot boundary detection in videos using Graph Cut Sets

The Shot Boundary Detection (SBD) is an early step for most of the video applications involving understanding, indexing, characterization, or categorization of video. The SBD is temporal video segmentation and it has been an active topic of research in the area of content based video analysis. The research efforts have resulted in a variety of algorithms. The major methods that have been used for shot boundary detection include pixel intensity based, histogram-based, edge-based, and motion vectors based, technique. Recently researchers have attempted use of graph theory based methods for shot boundary detection. The proposed algorithm is one such graph based model and employs graph partition mechanism for detection of shot boundaries. Graph partition model is one of the graph theoretic segmentation algorithms, which offers data clustering by using a graph model. Pair-wise similarities between all data objects are used to construct a weighted graph represented as an adjacency matrix (weighted similarity matrix) that contains all necessary information for clustering. Representing the data set in the form of an edge-weighted graph converts the data clustering problem into a graph partitioning problem. The algorithm is experimented on sports and movie videos and the results indicate the promising performance

A rapid and robust method for shot boundary detection and classification in uncompressed MPEG video sequences

Abstract Shot boundary and classification is the first and most important step for further analysis of video content. Shot transitions include abrupt changes and gradual changes. A rapid and robust method for shot boundary detection and classification in MPEG compressed sequences is proposed in this paper. We firstly only decode I frames partly in video sequences to generate DC images and then calculate the difference values of histogram of these DC images in order to detect roughly the shot boundary. Then, for abrupt change detection, shot boundary is precisely located by movement information of B frames. Shot gradual change is located by difference values of successive N I frames and classified by the alteration of the number of intra coding macroblocks (MBs) in P frames. All features such as the number of MBs in frames are extracted from uncompressed video sequences. Experiments have been done on the standard TRECVid video database and others to reveal the performance of the proposed method

Video Categorization Using Data Mining

Video categorization using data mining is the area of the research that aims to propose adeveloped method based on Artificial Neural Network (ANN), which could be used to classify video files into different categories according to the content. In order to test this method, the classifications of video files are discussed. The applied system proposes that the video could be categorized in two classes. The first one is educational while is noneducational. The classification is conducted based on the motion using optical flow. Several experiments were conducted using Artificial Neural Network (ANN) model. The research facilitate access to the required educational video to the learners students, especially novice students. This research objective is to investigate how the effect of motion feature can be useful in such lassification. We believe that other effects such audio features, text features, and other factors can enhance accuracy, but this requires wider studies and need more time. The accuracy of results in video classification to educational and non-educational through technique 3 fold cross validation and using (ANN) model is 54%. This result may can be improved by introducing other factors mentioned above

Adaptive Edge-Oriented Shot Boundary Detection

We study the problem of video shot boundary detection using an adaptive edge-oriented framework. Our approach is distinct in its use of multiple multilevel features in the required processing. Adaptation is provided by a careful analysis of these multilevel features, based on shot variability. We consider three levels of adaptation: at the feature extraction stage using locally-adaptive edge maps, at the video sequence level, and at the individual shot level. We show how to provide adaptive parameters for the multilevel edge-based approach, and how to determine adaptive thresholds for the shot boundaries based on the characteristics of the particular shot being indexed. The result is a fast adaptive scheme that provides a slightly better performance in terms of robustness, and a five fold efficiency improvement in shot characterization and classification. The reported work has applications beyond direct video indexing, and could be used in real-time applications, such as in dynamic monitoring and modeling of video data traffic in multimedia communications, and in real-time video surveillance. Experimental results are included.</p