Region Expansion Algorithm: A Well-Quality Region Proposal Generation

Abstract

This paper proposes an efficient algorithm to appropriately generate a limited number of regions, socalled a region proposal algorithm, for resolving computer vision problems. The most important challenge in region proposal technique is to accurately produce a limited number of regions. This literature introduces an algorithm to answer to the challenge through expanding region. The proposed algorithm comprises of two components and in a triple way. The first component divides an image into some non-overlapping regions. Afterwards, each region is developed in adjacent regions based on the 8-connectivity in the second component. In fact, the image is represented by overlapping expanded regions. This component can be executed to three different modes as fixed, all, and efficient-mode. Our proposed algorithm shows better results than existing state-of-the-art segmentation algorithms, including EGBS, Quick shift, and SLIC. The quality of the regions is measured according to the best overlap and recall metrics at MSRC dataset. The results show the good achievement of the overlap and recall as well. The best result is acquired by EGBS and efficient-mode around 24% and 13% improvement for recall and the best overlap, respectively.