Object Detection in Images Based on Homogeneous Region Segmentation
Image segmentation for object detection is one of the most fundamental problems in computer vision, especially in object-region extraction task. Most popular approaches in the segmentation/object detection tasks use sliding-window or super-pixel labeling methods. The first method suffers from the number of window proposals, whereas the second suffers from the over-segmentation problem. To overcome these limitations, we present two strategies: the first one is a fast algorithm based on the region growing method for segmenting images into homogeneous regions. In the second one, we present a new technique for similar region merging, based on a three similarity measures, and computed using the region adjacency matrix. All of these methods are evaluated and compared to other state-of-the-art approaches that were applied on the Berkeley image database. The experimentations yielded promising results and would be used for future directions in our work.
Region proposal, Region growing, Region merging, Image segmentation