Developing an integrated video analysis system Matching the similarity between two units of data occurs as a frequent task in video or image analysis. The parameters of matching techniques are level of abstraction of features, distance measures and normalization of features, if supported, or else the method of relatively weighing the features. Most multimedia analysis systems employ only low level features with distance measures similar to Euclidean distance, with no method to automatically generate the weights of the features and thus are ineffective in replenishing suitable matches to the useru0027s demands. We argue for shifting the burden of mapping the feature space with relevant categories from the user to the multimedia analysis system. In this paper, a Bayesian Framework is presented where the evaluation of the parameters of classification and especially the relevancy of each feature with respect to each class is performed automatically. The probabilistic framework is extended to work well for generalized multimodal distribution of a particular class over the feature space. Theoretical foundation is developed to provide simultaneously existing multiple views to an image or a video sequence. The lowlevel features can be synthesized with intelligent association to furnish highlevel features, which could be more meaningful to the user. The significance of this work is presented by comparing with a system which employs a unsophisticated approach similar to common systems where feature vector of query image and feature vector of template image are compared by means of weighted Euclidean distance. The superiority of our approach is presented over the database consisting of 300 video sequences comprising of diverse video classes.

A Fast and Efficient Framework for Indexing and Detection of Modified Copies in Video In this paper, we propose a framework for detecting near duplicate copies of a video based on an ordinal method. The framework also incorporates a bitmap indexing structure instead of a typical indexing structure used in the previous published work. Using this method, two levels of indices are constructed. The first level of this process groups each input video (represented by their key frames) into k clusters. These clusters and the associated key frames are then used to construct the first level index. The second level of this process converts ordinalbased video signatures (generated using the technique developed in earlier work) into bitmap vectors. By adopting this twolevel indexing scheme, query processing times are significantly reduced. This is because, the system is required to match only videos in the clusters that are relevant to the query and not all the videos in the database. Additionally, the technique implemented utilizes a bitmap structure for indexing, resulting in less storage space. Furthermore, we are able to employ lowcost Boolean operations such as AND, OR, and XOR in the matching process instead of Euclidean distance or other similar matching algorithms. This helps to reduce the computational time for video matching. The system has demonstrated to effectively reduce the space needed to store collections of video signatures in a database, as well as improving the overall system performance. In addition, initial results show that the system is effective and robust to several transformations such as changes in brightness, color, contrast, resolution (reduction) as well as the addition of noise.