A comprehensive overview of relevant methods of image cosegmentation

摘要

Segmenting the foreground objects from an image is an essential low-level step for many expert and intelligent systems, and the success of this key process largely depends on the amount of available training data. However, the cost for obtaining annotations is still a bottleneck, requiring enormous human effort. Thus, in order to obtain higher segmentation accuracy without the need of annotations, computer vision researchers attempted to segment simultaneously the common regions from collections of images. Indeed, when images that share common foreground objects are available, we talk about cosegmentation. The task of cosegmentation can be considered as weakly supervised segmentation, seen that the input image set is sharing common foreground objects. In fact, several methods have been proposed while displaying different frameworks, models and strategies of work, for enhancing the cosegmentation accuracy. This work presents a comprehensive review of relevant cosegmentation methods, carried out extensively and in depth and completed by performance measurements. Indeed, the existing cosegmentation methods are roughly classified in this paper into the following categories: Markov random fields-based cosegmentation, Co-saliency-based cosegmentation, Image decomposition-based cosegmentation, Random Walker-based cosegmentation, Maps-based cosegmentation, Active contours-based cosegmentation, Clustering-based cosegmentation and Deep learning-based cosegmentation. For each category, we discuss the most relevant methods while presenting the proposed problem to deal, the degree of supervision, the adopted features, the number and the complexity of input images as well as the presented models and frameworks. Then, in order to exhibit and evaluate objectively and comprehensively the most relevant state-of-the-art methods, we provide a comparative study on various challenging datasets (iCoseg, MSRC, Internet, FlickrMFC and PASCAL-VOC datasets). The objective is to show the performances and the limitations of these methods, while using different metrics and providing the computation cost for many methods. In addition, we discuss various cosegmentation challenges, issues and some applications, what can be useful and helpful to understand the cosegmentation problem. Finally, we suggest some research directions for future research on image cosegmentation. (C) 2019 Elsevier Ltd. All rights reserved.