Fast Image Retrieval Based on Equal-average Equal-variance K-Nearest Neighbour Search

This paper presents two fast schemes to speed up the retrieval process for conventional content-based image retrieval systems. The traditional features such as color and invariant histograms are extracted offline from each image to compose a feature vector. All these feature vectors construct the feature database. Then the system performs the online retrieval based on this database as soon as possible. In the case of a small number of returned images, an equal-average equalvariance K nearest neighbour search (EEKNNS) method is used to speed up the retrieval process. In the case of a large number of returned images, an iterative EEKNNS (IEEKNNS) method is given. Experimental results show that the proposed retrieval methods can largely accelerate the retrieval process while guaranteeing the same recall and precision.