Unsupervised multi-class co-segmentation via joint object detection and segmentation with energy minimization

Lei Li; Xuan Fei; Zhuoli Dong; Dexian Zhang

doi:10.1117/12.2210737

14 December 2015 Unsupervised multi-class co-segmentation via joint object detection and segmentation with energy minimization

Lei Li, Xuan Fei, Zhuoli Dong, Dexian Zhang

Author Affiliations +

Proceedings Volume 9812, MIPPR 2015: Automatic Target Recognition and Navigation; 981214 (2015) https://doi.org/10.1117/12.2210737
Event: Ninth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2015), 2015, Enshi, China

Abstract

Multi-class co-segmentation is a challenging task because of the variety and complexity of the objects and images. To get more accurate object proposals is the key step for the existing co-segmentation methods to obtain better performance. In this paper, we propose a novel method to co-segment multiple regions from a group of images in an unsupervised way. The key idea is to discover unknown object proposals for each image via joint object detection and object-level segmentation. First, object proposals of each image are generated by object-like windows (or boxes) and object-level segmentation using graph cuts, and two Gaussian mixture models (GMMs) are employed to characterize the object proposals for all images and single image, respectively. Then, a weighted graph for each image is constructed on super-pixel level, and multi-label graph cuts with global and local energy is employed to obtain the final co-segmentation results. In contrast to previous methods, our method could obtain the object proposals with high objectness by object-level segmentation. Experimental results demonstrate the good performance of the proposed method on the multi-class co-segmentation.

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Lei Li, Xuan Fei, Zhuoli Dong, and Dexian Zhang "Unsupervised multi-class co-segmentation via joint object detection and segmentation with energy minimization", Proc. SPIE 9812, MIPPR 2015: Automatic Target Recognition and Navigation, 981214 (14 December 2015); https://doi.org/10.1117/12.2210737

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