Elastic Net Sparse Coding-based Space Object Recognition

SHI Jun; JIANG Zhiguo; FENG Hao; ZHANG Haopeng; MENG Gang

doi:10.7527/S1000-6893.2013.0202

ACTA AERONAUTICAET ASTRONAUTICA SINICA >

2013 , Vol. 34 >Issue 5: 1129 - 1139

DOI: https://doi.org/10.7527/S1000-6893.2013.0202

Electronics and Control

Elastic Net Sparse Coding-based Space Object Recognition

SHI Jun ,
JIANG Zhiguo ,
FENG Hao ,
ZHANG Haopeng ,
MENG Gang

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1. School of Astronautics, Beihang University, Beijing 100191, China;
2. Beijing Key Laboratory of Digital Media, Beijing 100191, China;
3. Beijing Institute of Remote Sensing Information, Beijing 100191, China

Received date: 2012-06-11

Revised date: 2012-12-19

Online published: 2013-01-09

Supported by

National Natural Science Foundation of China (61071137, 61071138, 61027004); National Basic Research Program of China (2010CB327900)

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Abstract

The traditional bag-of-features (BoF) model for object recognition assumes each local feature point is related to only one visual word. Besides, sparse coding with l₁-norm constraint generally selects only one feature without concern for which one is selected. A novel bag-of-features model based on elastic net sparse coding is presented in this paper. The model uses scale invariant feature transform (SIFT) feature descriptors to construct a feature dictionary, and then applies an elastic net regression model to the solution of sparse-coefficient vectors. Finally the sparse-coefficient vectors in each object image are pooled for classification. Compared with the conventional BoF model and the BoF model based on l₁-norm sparse coding, our model achieves better recognition performance and is more robust to the variation of viewpoints. Experiments on the space object image database demonstrate the effectiveness of the proposed model.

Key words： bag-of-features; l₁-norm constraint; sparse coding; elastic net regression; space object recognition; feature extraction

Cite this article

SHI Jun , JIANG Zhiguo , FENG Hao , ZHANG Haopeng , MENG Gang . Elastic Net Sparse Coding-based Space Object Recognition[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2013 , 34(5) : 1129 -1139 . DOI: 10.7527/S1000-6893.2013.0202

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