Multi-label image annotation via Maximum Consistency

Wang, Hua; Hu, Jian

doi:10.1109/icip.2010.5649863

Cited by 7 publications

(1 citation statement)

References 6 publications

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“…Zha et al [60] proposed a graph based multi-label learning approach for image annotation. Wang et al [26] proposed a multi-label learning approach via maximum consistency. Chen et al [21] proposed an efficient multi-label learning based on hypergraph regularization.…”

Section: Related Workmentioning

confidence: 99%

Tag Completion for Image Retrieval

Jin

Jain

2013

IEEE Trans. Pattern Anal. Mach. Intell.

211

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Abstract-Many social image search engines are based on keyword/tag matching. This is because tag based image retrieval (TBIR) is not only efficient but also effective. The performance of TBIR is highly dependent on the availability and quality of manual tags. Recent studies have shown that manual tags are often unreliable and inconsistent. In addition, since many users tend to choose general and ambiguous tags in order to minimize their efforts in choosing appropriate words, tags that are specific to the visual content of images tend to be missing or noisy, leading to a limited performance of TBIR. To address this challenge, we study the problem of tag completion where the goal is to automatically fill in the missing tags as well as correct noisy tags for given images. We represent the image-tag relation by a tag matrix, and search for the optimal tag matrix consistent with both the observed tags and the visual similarity. We propose a new algorithm for solving this optimization problem. Extensive empirical studies show that the proposed algorithm is significantly more effective than the state-of-the-art algorithms. Our studies also verify that the proposed algorithm is computationally efficient and scales well to large databases.

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Section: Related Workmentioning

confidence: 99%