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Multi-instance multi-label image classification: A neural approach
- Source :
-
Neurocomputing . Jan2013, Vol. 99, p298-306. 9p. - Publication Year :
- 2013
-
Abstract
- Abstract: In this paper, a multi-instance multi-label algorithm based on neural networks is proposed for image classification. The proposed algorithm, termed multi-instance multi-label neural network (MIMLNN), consists of two stages of MultiLayer Perceptrons (MLP). For multi-instance multi-label image classification, all the regional features are fed to the first-stage MLP, with one MLP copy processing one image region. After that, the MLP in the second stage incorporates the outputs of the first-stage MLPs to produce the final labels for the input image. The first-stage MLP is expected to model the relationship between regions and labels, while the second-stage MLP aims at capturing the label correlation for classification refinement. Error Back-Propagation (BP) approach is adopted to tune the parameters of MIMLNN. In view of that traditional gradient descent algorithm suffers from long-term dependency problem, a refined BP algorithm named Rprop is extended to effectively train MIMLNN. The experiments are conducted on a synthetic dataset and the Corel dataset. Experimental results demonstrate the superior performance of MIMLNN comparing with state-of-the-art algorithms for multi-instance multi-label image classification. [Copyright &y& Elsevier]
Details
- Language :
- English
- ISSN :
- 09252312
- Volume :
- 99
- Database :
- Academic Search Index
- Journal :
- Neurocomputing
- Publication Type :
- Academic Journal
- Accession number :
- 82681255
- Full Text :
- https://doi.org/10.1016/j.neucom.2012.08.001