MMAP: modified maximum a posteriori algorithm for image segmentation in large image/video databases

Block-based feature extraction and clustering algorithms usually have to trade off between resolution and accuracy, as larger image block tends to generate more representative features at the expense of clustering resolution. We propose a new postprocessing technique for optimally combining the labeling results from overlapping image regions. This technique, the modified maximum a posteriori (MMAP) method, utilizes both local and global information from the neighborhood of the image region under consideration. Consequently, the resolution of the clustering becomes independent of the accuracy of the feature. Experimental results show dramatic improvement of the classification accuracy over methods that do not postprocess the clustering labels with the MMAP algorithm