Improved Performance in CBIR using Machine Learning Approach
B. Ramesh Naik, Temberveni Venugopal · 2018 International Conference on Control, Power, Communication and Computing Technologies (ICCPCCT) · 2018
We proposed a new approach which improves the high level image semantics based on machine learning approach. The contemporary approaches for image retrieval and object recognition includes Fourier transforms, Wavelets, SIFT and HoG. Though these descriptors helpful in a wide range of applications, they exploits zero order statistics and this lacks high descriptiveness of image features. These descriptors usually take benefit of primitive visual features such as shape, color, texture and spatial locations to describe images. These features do not adequate to describe high level semantics of the images. This leads to gap in semantic content caused to unacceptable performance in image retrieval system. A novel method has been proposed referred to as discriminative learning which is derived from machine learning approach that efficiently discriminates image features. The analysis and results of proposed approach were validated thoroughly on WANG and Caltech-101 Databases. The results provide evidence that this approach is very competitive in Image retrieval system.