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The objective of pattern matching problem is to find the most similar image pattern in a scene image by matching to an instance of the given pattern. For pattern matching, most distinctive features are computed from a pattern that is to be searched in the scene image. Scene image is logically divided into sliding windows of pattern size, and all the sliding windows are to be checked with the pattern for matching. Due to constant matching between the pattern and the sliding window, the matching process should be very efficient in terms of space, time and impacts due to orientation, illumination and occlusion must be minimized to obtain better matching accuracy. This paper presents a novel local feature descriptor called Multi-variant Local Binary Pattern (MVLBP) for pattern matching process while LBP is considered as base-line technique. The efficacy of the proposed pattern matching algorithm is tested on two databases and proved to be a computationally efficient one.

This paper reports a fast pattern matching algorithm which makes use of K-NN (K-nearest neighbor) based rank order reduction approach to detect a pattern or object in a given image efficiently. Initially, the given image is divided into several candidate windows, each of size the input pattern. In the next step, both the input pattern and the candidate windows are characterized by Haar transform. From the characterization, Haar Projection Values (HPV) is determined. Further, rectangle sum on both input pattern and candidate windows is computed using integral image technique. Subsequently, by using sum of absolute difference (SAD) correlation distance between the input pattern and candidate windows is determined. In order to detect the pattern, rank order approach using K-NN is applied to determine the first k number of most similar candidate windows containing the input pattern. To reduce the computational complexity of selecting a perfectly matched window, again sum of absolute differences (SAD) is applied and this leads to select the best match pattern having the total object. Decoupling correlation measures also increase the accuracy of matching pattern. Finally, the input pattern is detected and localized in the given image. The pattern matching accuracy proves the efficacy of the proposed algorithm.