Please use this identifier to cite or link to this item: https://doi.org/10.1145/1734605.1734613
Title: Hidden-concept driven image decomposition towards semi-supervised multi-label image annotation
Authors: Bao, B.-K. 
Li, T.
Yan, S. 
Keywords: Hidden-concepts
Multi-label image annotation
Nonnegative data factorization
Semi-supervised learning
Sparse coding
Issue Date: 2009
Source: Bao, B.-K.,Li, T.,Yan, S. (2009). Hidden-concept driven image decomposition towards semi-supervised multi-label image annotation. 1st International Conference on Internet Multimedia Computing and Service, ICIMCS 2009 : 17-24. ScholarBank@NUS Repository. https://doi.org/10.1145/1734605.1734613
Abstract: Conventional semi-supervised learning algorithms over multi-label image data propagate labels predominantly via the holistic image similarities, ignoring that each label essentially only characterizes a local region within an image. In this paper, we present a novel propagation-by-decomposition solution to this problem with the following characteristics: 1) each image representation is implicitly decomposed into several label representations; 2) those decompositions are guided by the so-called hidden concepts, which are expected to characterize image regions and be able to reconstruct both visual and non-visual labels of the entire image label space; 3) the intra-label diversity is expressed by the hidden-concept-specific subspace, which acts as the intermediate entity for propagating specific label from labeled data to unlabeled ones; and 4) the sparse coding based graph is proposed to enforce the collective consistency between image labels and image representations, which naturally avoids the dilemma of possible inconsistency between the pairwise label similarity and image representation similarity in multi-label scenario. These properties are finally embodied in a regularized nonnegative data factorization formulation, from which a convergence provable updating procedure is presented to iteratively optimize the objective function. Extensive experiments on three benchmark image datasets well validate the effectiveness of our proposed solution to semi-supervised multi-label image annotation problem. Copyright 2009 ACM.
Source Title: 1st International Conference on Internet Multimedia Computing and Service, ICIMCS 2009
URI: http://scholarbank.nus.edu.sg/handle/10635/70464
ISBN: 9781605588407
DOI: 10.1145/1734605.1734613
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