An Efficient Pattern Matching Approach Using Double Measures of Correlation and Rank Reduction

This paper discusses an efficient pattern matching approach on the use of K-nearest neighbour (K-NN) based rank order reduction and Haar transform in order to detect a pattern in a large scene image. To accomplish the task, scene image is divided into a number of candidate windows and both input pattern and candidate windows are characterised by Haar transform. This characterisation seeks to determine distinctive coefficients known as Haar projection values (HPVs). To obtain more relevant and useful representation of HPVs, rectangle sum is computed and further, sum of absolute (SAD) correlation measure is applied as successive measures between the input pattern and candidate windows. This leads to increase the possibility of finding the object in the scene image before being detected and localised. The proposed pattern matching approach is tested on COIL-100 database and the matching accuracy proves the efficacy of the proposed algorithm.