Proceedings Abstracts of the Twenty-Fourth International Joint Conference on Artificial Intelligence

Weakly Supervised Matrix Factorization for Noisily Tagged Image Parsing / 3749
Yulei Niu, Zhiwu Lu, Songfang Huang, Peng Han, Ji-Rong Wen

In this paper, we propose a Weakly Supervised Matrix Factorization (WSMF) approach to the problem of image parsing with noisy tags, i.e., segmenting noisily tagged images and then classifying the regions only with image-level labels. Instead of requiring clean but expensive pixel-level labels as strong supervision in the traditional image parsing methods, we take noisy image-level labels as weakly-supervised constraints. Specifically, we first over-segment all the images into multiple regions which are initially labeled based upon the image-level labels. Moreover, from a low-rank matrix factorization viewpoint, we formulate noisily tagged image parsing as a weakly supervised matrix factorization problem. Finally, we develop an efficient algorithm to solve the matrix factorization problem. Experimental results show the promising performance of the proposed WSMF algorithm in comparison with the state-of-the-arts.