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Retrieval-Based Face Annotation by Weak Label Regularized Local Coordinate Coding
March 2014 (vol. 36 no. 3)
pp. 550-563
Dayong Wang, Sch. of Comput. Eng., Nanyang Technol. Univ. Singapore, Singapore, Singapore
Steven C. H. Hoi, Sch. of Comput. Eng., Nanyang Technol. Univ. Singapore, Singapore, Singapore
Ying He, Sch. of Comput. Eng., Nanyang Technol. Univ. Singapore, Singapore, Singapore
Jianke Zhu, Coll. of Comput. Sci. & Technol., Zhejiang Univ., Hangzhou, China
Tao Mei, Microsoft Res. Asia, Beijing, China
Jiebo Luo, Dept. of Comput. Sci., Univ. of Rochester, Rochester, NY, USA
Auto face annotation, which aims to detect human faces from a facial image and assign them proper human names, is a fundamental research problem and beneficial to many real-world applications. In this work, we address this problem by investigating a retrieval-based annotation scheme of mining massive web facial images that are freely available over the Internet. In particular, given a facial image, we first retrieve the top n similar instances from a large-scale web facial image database using content-based image retrieval techniques, and then use their labels for auto annotation. Such a scheme has two major challenges: 1) how to retrieve the similar facial images that truly match the query, and 2) how to exploit the noisy labels of the top similar facial images, which may be incorrect or incomplete due to the nature of web images. In this paper, we propose an effective Weak Label Regularized Local Coordinate Coding (WLRLCC) technique, which exploits the principle of local coordinate coding by learning sparse features, and employs the idea of graph-based weak label regularization to enhance the weak labels of the similar facial images. An efficient optimization algorithm is proposed to solve the WLRLCC problem. Moreover, an effective sparse reconstruction scheme is developed to perform the face annotation task. We conduct extensive empirical studies on several web facial image databases to evaluate the proposed WLRLCC algorithm from different aspects. The experimental results validate its efficacy. We share the two constructed databases "WDB" (714,454 images of 6,025 people) and "ADB" (126,070 images of 1,200 people) with the public. To further improve the efficiency and scalability, we also propose an offline approximation scheme (AWLRLCC) which generally maintains comparable results but significantly reduces the annotation time.
Index Terms:
Face,Encoding,Optimization,Vectors,Sparse matrices,Image databases,Image coding,weak label,Face annotation,content-based image retrieval,machine learning,label refinement,web facial images
Citation:
Dayong Wang, Steven C. H. Hoi, Ying He, Jianke Zhu, Tao Mei, Jiebo Luo, "Retrieval-Based Face Annotation by Weak Label Regularized Local Coordinate Coding," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 36, no. 3, pp. 550-563, March 2014, doi:10.1109/TPAMI.2013.145
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