自然图像中的感兴趣目标检测技术

摘要

Saliency map based the Region of Interesting(ROI) detection often has the problems of not able to locate object of interesting accurately and that many interesting object can be detected on the same ROI. A new technique for detecting regions of interest in a natural image by using visual attention model and Fuzzy Support Vector Machine(FSVM) is proposed. A visual window including single object is created according to the visual attention and edge information based on distribution of corner points from an image. By using FSVM based on affinity among samples, it can extract single object in the visual window. Experimental results show that it coincides with human visual attention mechanism and demonstrate the effectiveness of the proposed approach.%基于显著图的目标检测方法不能精确地找到感兴趣目标的位置,或在同一感兴趣目标上检测出多个感兴趣区域.为此,提出一种视觉注意机制和模糊支持向量机(FSVM)相结合的算法.根据显著度和角点分布信息,从图像中获得包括单个目标的视觉窗口,并在窗口中采用FSVM算法分割目标和背最.实验结果表明,该方法符合生物的视觉注意机制,分割效果较好.