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We propose an inter-sequence matching method for exact and similarity matching of image sequences. Our method transforms the image sequence matching problem into matching sequences of real numbers. The method does not require sequences to be of the same length. It uses a modified version of the Longest Common Subsequence (LCS) method for actually matching two sequences. We also propose a feature-based indexing mechanism to filter out those sequences which are matching candidates with a given query sequence from a large data set. Like all other feature-based indexing methods, our method maps each sequence into a point in K dimensional space, where K is the number of extracted features for the sequence. It operates in two phases, hypothesizing and verification. Lengths and moments (mean and variance) of sequences are used as features. Experimental results indicate that the features and proposed method for query processing do well as a filter.