Feature Description and Image Retrieval Based on Visual Attention Model
Yunqi Lei, Xiaoling Gui, Zhenxiang Shi · Journal of Multimedia · 2011
B ased on the bottom-up human visual attention model, a procedure for feature description and image retrieval is proposed. In the procedure, an integrated saliency map for a given image , which reflects the significance of regions in the image, is generated first, then two region segmentation methods, the image segmentation using the maximum entropy of the one-dimensional histogram and the region growing based on the focus of attention, are defined and used to separate the image into different regions. Here, the focus of attention is obtained from the saliency map and the secondary or uncorrelated regions can be filtered then . Hereafter, combining the saliency map and the segmented image, the first three most salient regions, defined as the salient region set, are obtained in term s of the average saliency of the regions. Radius entropy and angle entropy are also defined and performed to describe the global feature s and a shape feature for saliency regions is used to describe the local feature. Finally, the distance s between the features are calculated to measure the similarity between images. Experimental results show ed that our method can reflect the semantic feature of a given image to a certain extent, and the method is beneficial for content-based image retrieval.