An Approach Based on Regression Line Features for Low Complexity Content Based Image Retrieval

Similarity matching is one of the important tasks in content based image retrieval systems. Similarity matching involves the computation of distance between the feature vectors characterizing the image samples. Conventional techniques like pixel based similarity matching are computationally costly and time consuming. In recent years the tremendous increase in multi media databases, especially image databases calls for fast and efficient image retrieval mechanisms. Multiresolution based approaches through multiresolution histograms and wavelet histograms proposed recently are proven to be computationally efficient. In this paper, we propose a methodology based on regression line features for further reducing the computational complexity of these multiresolution histogram based techniques