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Joint multi-label multi-instance learning for image classification
Abstract
In real world, an image is usually associated with multiple labels which are characterized by different regions in the image. Thus image classification is naturally posed as both a multi-label learning and multi-instance learning problem. Different from existing research which has considered these two problems separately, we propose an integrated multi-label multi-instance learning (MLMIL) approach based on hidden conditional random fields (HCRFs), which simultaneously captures both the connections between semantic labels and regions, and the correlations among the labels in a single formulation. We apply this MLMIL framework to image classification and report superior performance compared to key existing approaches over the MSR Cambridge (MSRC) and Corel data sets.
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Citations
Cites
Training Products of Experts by Minimizing Contrastive Divergence
Hinton, G. E. (2002) · Neural Computation · doi:10.1162/089976602760128018 · Cited by 4,879 · cited 1×
“Here we resort to contrastive divergence (CD) algorithm [12], which only needs to take a few steps in the Markov chain to approximate the gradients.”
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Cited by
Multiple instance learning: A survey of problem characteristics and applications
Carbonneau, M.-A., Cheplygina, V., Granger, E., & Gagnon, G. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2017.10.009 · Cited by 740
“Efforts were dedicated to localize objects and segment them at pixel-level using traditional segmentation algorithms such as Constraint Parametric Min-Cuts [110], JSEG [74] or Multi-scale combinatorial grouping [111].”
MethodPedestrian Attribute Recognition At Far Distance
DENG, Y., Luo, P., Loy, C. C., & Tang, X. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654966 · Cited by 448
Grauman, K., & Leibe, B. (2011) · Synthesis Lectures on Artificial Intelligence and Machine Learning · doi:10.2200/s00332ed1v01y201103aim011 · Cited by 126
Mapping visual features to semantic profiles for retrieval in medical imaging
Hofmanninger, J., & Langs, G. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298643 · Cited by 38
Metadata
- Authors
- Zha, Z.-J., Hua, X.-S., Mei, T., Wang, J., Qi, G.-J., & Wang, Z.
- Venue
- 2008 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2008-06
- Pages
- 1-8
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2164736477
- Cited by
- 219
- References
- 1
- FWCI
- 12.02
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Zha, Z.-J., Hua, X.-S., Mei, T., Wang, J., Qi, G.-J., & Wang, Z. (2008). Joint multi-label multi-instance learning for image classification. 2008 IEEE Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2008.4587384
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