Please use this identifier to cite or link to this item: https://doi.org/10.1145/1873951.1873959
Title: Efficient large-scale image annotation by probabilistic collaborative multi-label propagation
Authors: Chen, X.
Mu, Y. 
Yan, S. 
Chua, T.-S. 
Keywords: collaborative multi-label propagation
image annotation
Issue Date: 2010
Source: Chen, X.,Mu, Y.,Yan, S.,Chua, T.-S. (2010). Efficient large-scale image annotation by probabilistic collaborative multi-label propagation. MM'10 - Proceedings of the ACM Multimedia 2010 International Conference : 35-44. ScholarBank@NUS Repository. https://doi.org/10.1145/1873951.1873959
Abstract: Annotating large-scale image corpus requires huge amount of human efforts and is thus generally unaffordable, which directly motivates recent development of semi-supervised or active annotation methods. In this paper we revisit this notoriously challenging problem and develop a novel multi-label propagation scheme, whereby both the efficacy and accuracy of large-scale image annotation are further enhanced. Our investigation starts from a survey of previous graph propagation based annotation approaches, wherein we analyze their main drawbacks when scaling up to large-scale datasets and handling multi-label setting. Our proposed scheme outperforms the state-of-the-art algorithms by making the following contributions. 1) Unlike previous approaches that propagate over individual label independently, our proposed large-scale multi-label propagation (LSMP) scheme encodes the tag information of an image as a unit label confidence vector, which naturally imposes inter-label constraints and manipulates labels interactively. It then utilizes the probabilistic Kullback-Leibler divergence for problem formulation on multi-label propagation. 2) We perform the multi-label propagation on the so-called hashing-based L1-graph, which is efficiently derived with Locality Sensitive Hashing approach followed by sparse L1-graph construction within the individual hashing buckets. 3) An efficient and convergency provable iterative procedure is presented for problem optimization. Extensive experiments on NUS-WIDE dataset (both lite version with 56k images and full version with 270k images) well validate the effectiveness and scalability of the proposed approach. © 2010 ACM.
Source Title: MM'10 - Proceedings of the ACM Multimedia 2010 International Conference
URI: http://scholarbank.nus.edu.sg/handle/10635/43228
ISBN: 9781605589336
DOI: 10.1145/1873951.1873959
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