Image segmentation improvement by reversible segment merging
Igor Georgievich Khanykov, Mikhail Vyacheslavovich Kharinov, Chirag Indravadanbhai Patel · 2017
The paper focuses on image segmentation by means of approaching by piecewise-constant approximations. The objective is the improvement of the segmented image, expressed in the noticeable drop of image approximation error (total squared error). The proposed method involves segment dividing into two in some image part and merge of a pair of adjacent segments in another image part, keeping a total segment number constant. For further enhancement of the segmentation, the proposed method is used in combination with advanced K-means method. The steep decline of the approximation error is provided along with obvious increase of perceptual quality of image segmentation. The effect is achieved owing to binary adaptive hierarchy of nested segments, generated for each segment. The segmentation improvement method is usable for the advancement of the computer vision systems using conventional segmentation by partitioning image into connected segments.