Saliency-Based Image Retrieval as a Refinement to Content-Based Image Retrieval



Searching for an image in a database is important in different applications; hence, many algorithms have been proposed to identify the contents of the image. In some applications, but not all, identifying the content of the image as a whole can offer good results. Searching for an object inside the image is more important in most applications than identifying the image as a whole. Therefore, studies focused on segmenting the image into small sub-images and identified their contents. In view of the concepts of human attention, various literature defined saliency as a computer representation of it, where different algorithms were developed to extract the salient regions. These salient regions, which are the regions that attract human attention, are used to identify the most important regions that contain important objects in the image. In this paper, we introduce a new algorithm that utilises the saliency principles to identify the contents of an image and search for similar objects in the images stored in a database. We also demonstrate that the use of salient objects produces better and more accurate results in the image retrieval process. A new retrieval algorithm is therefore presented here, focused on identifying the objects extracted from the salient regions. To assess the efficiency of the proposed algorithm, a new evaluation method is also proposed which considers the order of the retrieved image in assessing the efficiency of the algorithm.


Image Saliency, Human Attention, Image Retrieval, Histogram Features

Author Biography

Mohammad A. N. Al-Azawi, Oman College of Management and Technology

Dr Mohammad Alazawi is the Assistant Dean for Academic Affairs at OCMT. He has MPhil & PhD in Computational Intelligence from De Montfort University/UK, in additional to his BSc and MSc in Computer Engineering. He has over 16 years of teaching experience. & has published many papers in leading journals and participated in many international Conferences. He is a reviewer and editor for many journals and conferences. His research interests: applying human intelligence on machine vision and AI.




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