Hesse Sparse Representation under n-words Model for Image Retrieval

WANG Ruixia; PENG Guohua

doi:10.11999/JEIT150617

Volume 38 Issue 5

May 2016

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(5): 1115-1122

WANG Ruixia, PENG Guohua. Hesse Sparse Representation under n-words Model for Image Retrieval[J]. Journal of Electronics & Information Technology, 2016, 38(5): 1115-1122. doi: 10.11999/JEIT150617

Citation:

WANG Ruixia, PENG Guohua. Hesse Sparse Representation under n-words Model for Image Retrieval[J]. Journal of Electronics & Information Technology, 2016, 38(5): 1115-1122. doi: 10.11999/JEIT150617

WANG Ruixia, PENG Guohua. Hesse Sparse Representation under n-words Model for Image Retrieval[J]. Journal of Electronics & Information Technology, 2016, 38(5): 1115-1122. doi: 10.11999/JEIT150617

Citation:

WANG Ruixia, PENG Guohua. Hesse Sparse Representation under n-words Model for Image Retrieval[J]. Journal of Electronics & Information Technology, 2016, 38(5): 1115-1122. doi: 10.11999/JEIT150617

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Hesse Sparse Representation under n-words Model for Image Retrieval

doi: 10.11999/JEIT150617

WANG Ruixia¹,
PENG Guohua¹

Funds:

The National Natural Science Foundation of China (61201323)

Received Date: 2015-05-20
Rev Recd Date: 2016-01-15
Publish Date: 2016-05-19

Abstract

Abstract

To deal with the problem that the Bag-Of-Visual-Words (BOVW) model discards image spatial structure, a new method based on the Hessian sparse coding for image retrieval is introduced. First, the n-words model is built in order to obtain the local feature representation. The n-words model can establish a high-level description using a series of visual word sequences to represent an image. The experiments are performed from n=1 to n=5 to seek the proper n. Second, the Hessian sparse coding formulation is acquired by incorporating the Hessian energy function into the standard sparse coding formulation. Finally, using the obtained n-words sequences as the encoding features, the optimal Hessian coefficients are calculated through the feature-sign search algorithm. The similarity is computed and the retrieval results are returned. The experiments are performed on the two datasets, the results show that the proposed new method for image retrieval outperforms the BOVW model and existent methods.
- Image retrieval,
- Sparse coding,
- Bag-Of-Visual-Words (BOVW) model,
- n-words model,
- Hesse energy function

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References(25)

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