Content-Based Image Retrieval using Encoder based RGB and Texture Feature Fusion

Abstract

Recent development of digital photography and the use of social media using smartphones has boosted the demand for image query by its visual semantics. Content-Based Image Retrieval (CBIR) is a well-identified research area in the domain of image and video data analysis. The major challenges of a CBIR system are (a) to derive the visual semantics of the query image and (b) to find all the similar images from the repository. The objective of this paper is to precisely define the visual semantics using hybrid feature vectors. In this paper, a CBIR system using encoded-based feature fusion is proposed. The CNN encoding features of the RGB channel are fused with the encoded texture features of LBP, CSLBP, and LDP separately. The retrieval performance of the different fused features is tested using three public datasets i.e. Corel-lK, Caltech, and 102flower. The result shows the class properties are better retained using the LDP with RGB encoded features, this helps to enhance the classification and retrieval performance for all three datasets. The average precision of Corel-lK is 94.5% and it is 89.7% for Caltech, and 88.7% for the 102flower. The average f1-score is 89.5% for Caltech, and 88.5% for the 102flower. The improvement in the f1-score value implies the proposed fused feature is more stable to deal the class imbalance problem.