Regional weighted comentropy and its application in image feature extraction
MA Zi-long · Journal of Computer Applications · 2009
Image feature extraction is the research focus in the field of content-based image retrieval;however,entropy-based image feature extraction cannot demonstrate the location of image content information. A new description method of image comentropy named regional weighted comentropy was proposed,which combined the concept of image comentropy and image segmentation algorithm after analyzing the current color-space image feature extraction algorithms. Some properties of regional weighted comentropy were proved. The distribution change of image comentropy,which was caused by weight's change,was described by using comentropy performance evaluation index in terms of probability,considering the interested regions and weights precision applied by users,then the reasonable weight was determined. Experimental results show that the accuracy of image content described by regional weighted comentropy method is more than 50% higher than that of traditional comentropy methods.