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Pattern matching problem aims to search the most similar pattern or object by matching to an instance of that pattern in a scene image. In order to address the issue of finding an object in the target image efficiently, the most distinctive features are computed from the query pattern and need to be searched in the scene image. The scene image is logically divided into a number of candidate windows which are then to be matched with the query pattern. Due to repeated matching of the query pattern with local candidate windows, the pattern matching process requires a large amount of space in memory as well as it needs to be executed fast. Thus, pattern matching algorithms need to be memory efficient and as fast as possible. This paper makes an attempt to deal with these issues by presenting two effective pattern matching algorithms, namely, strip subtraction and strip division. The efficacy of the proposed pattern matching algorithms is tested on two databases, viz. a local database and MIT-CSAIL database containing random objects. The experimental results are proved to be computationally efficient ones while the proposed algorithms are compared with some existing algorithms possessing a uniform experimental setup.

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