A Competent and Novel Approach of Designing an Intelligent Image Retrieval System

Abstract

A prominent technique used to search imperceptible images from any vast repository is denoted by Content based image retrieval (CBIR) system. The most eminent features of CBIR system are Texture, Color and Shape and in this paper these features are extracted using Local binary pattern, Color moment and Automatic segmentation process respectively. The features of these descriptors are combined for the formation of a hybrid feature vector by utilizing the process of normalization. Then, for enhancing the retrieval accuracy of the proposed system, Extreme learning machine (ELM) has been utilized as a classifier. The proposed hybrid CBIR system with ELM leads to an evolution of an intelligent system. Various evaluation metrics like Precision, Recall, Accuracy, etc. have been calculated on the proposed system on standard datasets. Average precision of 90%, 78%, 72% and 88.70% has been obtained respectively on Corel-1K, Corel-5K, Corel-10K and GHIM datasets which is significantly higher than the related techniques.

A Hybrid ACO based Optimized RVM Algorithm for Land Cover Satellite Image Classification

“…To reach the same accuracy, other optimization methods require significantly more iterations. The fitness value is valued as 92%, and from this evaluation, it is concluded that the proposed ACO method discovers an optimum value for the RVM parameter within a few iterations [19]. Fig.…”