Content-based image retrieval using computational visual attention model

• DOI: 10.1016/j.patcog.2015.02.005
• Publication Date: 2015-02-16T17:46:01Z
• Authors: Guang-Hai Liu; Jing-Yu Yang; ZuoYong Li
• Keywords: 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology

It is a very challenging problem to well simulate visual attention mechanisms for content-based image retrieval. In this paper, we propose a novel computational visual attention model, namely saliency structure model, for content-based image retrieval. First, a novel visual cue, namely color volume, with edge information together is introduced to detect saliency regions instead of using the primary visual features (e.g., color, intensity and orientation). Second, the energy feature of the gray-level co-occurrence matrices is used for globally suppressing maps, instead of the local maxima normalization operator in Itti?s model. Third, a novel image representation method, namely saliency structure histogram, is proposed to stimulate orientation-selective mechanism for image representation within CBIR framework. We have evaluated the performances of the proposed algorithm on two datasets. The experimental results clearly demonstrate that the proposed algorithm significantly outperforms the standard BOW baseline and micro-structure descriptor. A novel computational visual attention model is developed for content-based image retrieval.Color volume is introduced to detect saliency areas.The energy feature of GLCM is used for globally suppressing map.Orientation-selective mechanism is stimulated for image representation.