Content Based Image Retrieval System by Fusion of Color, Texture and Edge Features With SVM Classifier and Relevance Feedback

Abstract

Content Based Image Retrieval system automatically retrieves the most relevant images to the query image by extracting the visual features instead of keywords from images. Over the years, several researches have been conducted in this field but the system still faces the challenge of semantic gap and subjectivity of human perception. This paper proposes the extraction of low-level visual features by employing color moment, Local Binary Pattern and Canny Edge Detection techniques for extracting color, texture and edge features respectively. The combination of these features is used in conjunction with Support Vector Machine to reduce the retrieval time and improve the overall precision. Also, the challenge of semantic gap between low and high level features is addressed by incorporating Relevance Feedback. Average precision value of 0.782 was obtained by combining the color, texture and edge features, 0.896 was obtained by using combined features with SVM, 0.882 was obtained by using combined features with Relevance Feedback to overcome the challenge of semantic gap. Experimental results exhibit improved performance than other state of the art techniques.

Efficient Content-Based Image Retrieval System with Two-Tier Hybrid Frameworks

“…The detailed Colour Coherence Vector (CCV) method is shown in [10]. In CCV method, both the coherent and incoherent vectors are used as image features, and the images are retrieved efficiently.…”

Generic Graphical User Interface for CBIR Framework

“…The exhaustive CBIR system and its classification can be found in [2]- [4]. Further, a detailed CBIR system with various methods of retrieval can be found in [5]- [10]. Though there has been work carried out on classical CBIR for image retrieval system, there is a requirement of user interactive and graphical means of image retrieval.…”