Content-based Image Retrieval Considering Colour Difference Histogram of Image Texture and Edge Orientation

Abstract

Content-based image retrieval is one of the interesting subjects in image processing and machine vision. In image retrieval systems, the query image is compared with images in the database to retrieve images containing similar content. Image comparison is done using features extracted from the query and database images. In this paper, the features are extracted based on the human visual system. Since the human visual system considers the texture and the edge orientation in images for comparison, the colour difference histogram associated with the image's texture and edge orientation is extracted as a feature. In this paper, the features are selected using the Shannon entropy criterion. The proposed method is tested using the Corel-5K and Corel-10K databases. The precision and recall criteria were used to evaluate the proposed system. The experimental results show the ability of the proposed system for more accurate retrieval rather than recently content-based image retrieval systems.

Probabilistic Twin Support Vector Machine for Solving Unclassifiable Region Problem

“…Pattern recognition and classification methods are applied to a vast range of real-world applications such as image classification [1][2][3], disease detection [4], text classification [5], and so on. The significant growth in these applications shows the necessity of fast and classifiers.…”