Study On Key Techniques Of Region-based Image Matching Algorithm

The key techniques on region-based image retrieval and matching algorithm is elucidated in detail in this paper. Several key phases in low-feature-based image retrieval are feature extraction, image segmentation, region representation and image matching. The method presented in this paper firstly partitions the image into equal parts in size 4×4 and extracts the color and texture feature from every part in total 6-dimensional feature vector. Secondly this method segments the image into regions using a clustering method named fuzzy C-means algorithm. For several categories of images which have the characteristics of easily distinguished object and background on color and texture, one method is presented used to merge the regions and the segmentation results show that this method is satisfying. For the purpose to lower the inaccuracy caused by the image segmentation, this paper uses a fuzzy feature representation method based on the Cauchy function to represent the region features. In the phase of region matching, this paper elucidates some matching algorithms commonly used and makes a contrast between the image retrieval efficiency based on the IRM and the UFM matching scheme. In order to improve the retrieval efficiency, for several categories of images which have large background areas and simple color and texture, this paper makes some improvements based on the IRM method. But unfortunately the result is not so satisfying. So this paper presents a more general algorithm based on IRM and the experimental results show that the retrieval efficiency is improved apparently.