Please use this identifier to cite or link to this item:
|Title:||Efficient large-scale image annotation by probabilistic collaborative multi-label propagation|
|Keywords:||collaborative multi-label propagation|
|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|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Dec 13, 2017
checked on Dec 16, 2017
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.