IEEE Trans Pattern Anal Mach Intell - Retrieval-based Face Annotation by Weak Label Regularized Local Coordinate Coding.

Tópicos

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Resumo

Retrieval-based face annotation is a promising paradigm of mining massive web facial images for automated face annotation. This paper addresses a critical problem of such paradigm, i.e., how to effectively perform annotation by exploiting the similar facial images and their weak labels which are often noisy and incomplete. In particular, we propose an effective Weak Label Regularized Local Coordinate Coding (WLRLCC) technique, which exploits the principle of local coordinate coding in learning sparse features, and employs the idea of graph-based weak label regularization to enhance the weak labels of the similar facial images. We present an efficient optimization algorithm to solve the WLRLCC task. We conduct extensive empirical studies on two large-scale web facial image databases: (i) a Western celebrity database with a total of $6,025$ persons and $714,454$ web facial images, and (ii)an Asian celebrity database with $1,200$ persons and $126,070$ web facial images. The encouraging results validate the efficacy of the proposed WLRLCC algorithm. To further improve the efficiency and scalability, we also propose a PCA-based approximation scheme and an offline approximation scheme (AWLRLCC), which generally maintains comparable results but significantly saves much time cost. Finally, we show that WLRLCC can also tackle two existing face annotation tasks with promising performance.

Resumo Limpo

retrievalbas face annot promis paradigm mine massiv web facial imag autom face annot paper address critic problem paradigm ie effect perform annot exploit similar facial imag weak label often noisi incomplet particular propos effect weak label regular local coordin code wlrlcc techniqu exploit principl local coordin code learn spars featur employ idea graphbas weak label regular enhanc weak label similar facial imag present effici optim algorithm solv wlrlcc task conduct extens empir studi two largescal web facial imag databas western celebr databas total person web facial imag iian asian celebr databas person web facial imag encourag result valid efficaci propos wlrlcc algorithm improv effici scalabl also propos pcabas approxim scheme offlin approxim scheme awlrlcc general maintain compar result signific save much time cost final show wlrlcc can also tackl two exist face annot task promis perform

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