Content-Based Image Retrieval Using Multiresolution Feature Descriptors

The advent of low-cost cameras and smartphones have made the task of image capturing quite easy nowadays. This has resulted in the collection of large number of unorganized images. Accessing images from large repository of unorganized images is quite challenging. There is a need of such systems which help in proper organization and easy access of images. The field of image retrieval, using text or image, attempts to solve this problem. While text-based retrieval systems are quite popular, they suffer from certain drawbacks. The other type of image retrieval system, which is Content-based Image Retrieval (CBIR) system, uses image features to search for relevant images. This chapter discusses the concept multiresolution feature descriptors for CBIR. For capturing varying level of details, single resolution processing of image proves to be insufficient. The use of multiresolution descriptors prove to be quite efficient in capturing complex foreground and background details in an image. This chapter discusses the important properties and advantages of multiresolution feature descriptors. Furthermore, this chapter proposes a CBIR technique using a novel multiresolution feature descriptor. The proposed method constructs feature vector by capturing shape feature in a localized manner. The experimental results show the effectiveness of the proposed method.