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Content based video indexing and retrieval traces back to the elementary video structures, such as a table of contents. Thus, algorithms for video partitioning have become crucial with the unremitting growth in the prevalent digital video technology. This demands for a tool which would break down the video into smaller and manageable units called shots. In this paper, a shot boundary detection technique has been proposed for abrupt scene cuts. The method computes cooccurrence matrices by taking block differences between the consecutive frames in each of R, G, and B plane, using sum of absolute differences (SAD). Feature vectors are extracted from the co-occurrence matrices' statistics, defined at various pixel displacement distances. The statistical find-outs are integrated into a training set and an unsupervised classifier, K-means, is used to identify the shot-frames and the non-shot frames.