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Multi-label learning has attracted significant interests in computer visionrecently, finding applications in many vision tasks such as multiple objectrecognition and automatic image annotation. Associating multiple labels to acomplex image is very difficult, not only due to the intricacy of describingthe image, but also because of the incompleteness nature of the observedlabels. Existing works on the problem either ignore the label-label andinstance-instance correlations or just assume these correlations are linear andunstructured. Considering that semantic correlations between images areactually structured, in this paper we propose to incorporate structuredsemantic correlations to solve the missing label problem of multi-labellearning. Specifically, we project images to the semantic space with aneffective semantic descriptor. A semantic graph is then constructed on theseimages to capture the structured correlations between them. We utilize thesemantic graph Laplacian as a smooth term in the multi-label learningformulation to incorporate the structured semantic correlations. Experimentalresults demonstrate the effectiveness of the proposed semantic descriptor andthe usefulness of incorporating the structured semantic correlations. Weachieve better results than state-of-the-art multi-label learning methods onfour benchmark datasets.

Improving Multi-label Learning with Missing Labels by Structured Semantic Correlations