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Video Shot boundary detection is the process of automatically detecting the boundaries between shots in video. Shot Boundary detection is an important fundamental process in video data access, indexing, search and retrieval. The increased availability and usage of on-line digital video has created a need for automated video content analysis techniques. Detection of gradual transition and elimination of disturbances caused by illumination change is a major challenge in shot boundary detection technique. These disturbances are often mistaken as shot boundaries. It is a crucial task to develop a method that is not only insensitive to illumination change but also sensitive to detect shot change. An algorithm is proposed for shot boundary detection in presence of illumination change. This is very important for accurate detection of shot boundaries and very useful in content based analysis of video. First the algorithm removes illumination change using discrete cosine transform and discrete wavelet transform. Then shot boundaries are detected using normal difference & wavelet difference. A shot boundary is detected when the feature difference shows sharp change greater than threshold. Experimental study is performed on number of videos that include significant illumination change. The performance of proposed algorithm is better as compared to existing techniques General TermsVideo shot boundary detection, Image processing, Pattern recognition.

CBIR system focuses on retrieving images from the database; the system depends on the way the indexing is being implemented. The way or method in which an image is stored will affect how it will be retrieved later and which can save more storage space and improve the retrieval process. Building effective content-based image retrieval (CBIR) systems involves the combination of image creation, storage, security, transmission, analysis, evaluation feature extraction, and feature combination in order to store and retrieve images effectively. The goal of CBIR systems is to support image retrieval based on content i.e. shape, color, texture. In this paper we have implemented CBIR techniques using conventional Histogram and Gabor filter. We have shown results of query image and retrieved image also 2D frequency response of Gabor filter with various angles as it is direction dependent filter. We have used Euclidean distance as a measure to calculate distance between two images.

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