Enhancing Scalability of Image Retrieval Using Visual Fusion of Feature Descriptors

Abstract

Content-Based Image Retrieval (CBIR) is an approach of retrieving similar images from a large image database. Recently CBIR poses new challenges in semantic categorization of the images. Different feature extraction technique have been proposed to overcome the semantic breach problems, however these methods suffer from several shortcomings. This paper contributes an image retrieval system to extract the local features based on the fusion of scale-invariant feature transform (SIFT) and KAZE. The strength of local feature descriptor SIFT complements global feature descriptor KAZE. SIFT concentrates on the complete region of an image using high fine points of features and KAZE ponders on details of a boundary. The fusion of local feature descriptor and global feature descriptor boost the retrieval of images having diverse semantic classification and also helps in achieving the better results in large scale retrieval. To enhance the scalability of image retrieval bag of visual words (BoVW) is mainly used. The fusion of local and global feature representations are selected for image retrieval for the reason that SIFT effectively captures shape and texture and robust towards the change in scale and rotation, while KAZE have strong response towards boundary and changes in illumination. Experiments conducted on two image collections, namely, Caltech-256 and Corel 10k demonstrate the proposed scheme appreciably enhanced the performance of the CBIR compared to state-of-the-art image retrieval techniques.