[1]ZHAO Qian,LI Min,ZHAO Xiaojie,et al.Learning receptive fields for compact bag-of-feature model[J].CAAI Transactions on Intelligent Systems,2016,11(5):663-669.[doi:10.11992/tis.201601001]
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Learning receptive fields for compact bag-of-feature model
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
11
Number of periods:
2016 5
Page number:
663-669
Column:
学术论文—自然语言处理与理解
Public date:
2016-11-01
- Title:
- Learning receptive fields for compact bag-of-feature model
- Keywords:
- bag-of-features model; receptive field learning; object recognition; image classification; feature learning
- CLC:
- TP391.4
- DOI:
- 10.11992/tis.201601001
- Abstract:
- In this work, the effects of receptive field learning on a bag-of-features pipeline were investigated for an image identification task. In a bag-of-features model, the receptive field of a feature depends mostly on use of visual words in a visual dictionary and the region used during the pooling process. Codewords make the feature respond to specific image patches and the pooling regions determine the spatial scope of the features. A modified graft feature selecting algorithm was proposed to find the most efficient receptive fields for identification purposes; this considers the redundancy problem created by simultaneously increasing visual words. Using learning receptive fields, inefficient and redundant codewords and pooling regions were found and subsequently eliminated from the pooling region, this made the pipeline more compact and separable for the specified classification task. The experiments show that the modified learning algorithm is effective and the learned pipeline useful for both structural simplification and improving classification accuracy compared with the baseline method.
- References:
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1900-01-01