Cuckoo Search Algorithm Based Feature Selection in Image Retrieval System

Abstract

Efficiency an d retrieval time are very important issues in any content-based image retrieval system. In this study, an efficient image retrieval system was introduced depending on several features extracted from the database images, namely color moment (Mean, Standard Deviation), GLCM, and DWT( only LL-sub band). To increase the retrieval speed, Cuckoo search algorithm was used to select the important positions that contain full power features from the (LL-sub band). On using the Cuckoo search algorithm, only (50) important positions were chosen out of the total (24576) positions within (LL- sub band). These positions were stored for later use when entering a query image. Thus, the time taken to retrieve images was greatly reduced and this process also increased the efficiency of the system due to the fact that the selected positions gave the lowest distance measures between the query images and the similar images when evaluated using Manhattan distance measure. Two effectual performance measures (precision & recall) were used to calculate the accuracy of the system. The findings proved the system efficiency when compared to other previous works.

Keywords: CBIR, Color Moment, GLCM, DWT, Cuckoo Search algorithm, Manhattan measure

DOI: 10.7176/JEP/10-15-08

Publication date:May 31^st 2019