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Natural scene recognition using weighted histograms of gradient orientation descriptor

  • Research Article
  • Published:
Frontiers of Electrical and Electronic Engineering in China

Abstract

The automatic recognition of the contents of a scene is an important issue in the computer vision field. Though considerable progress has been made, the complexity of scenes remains an important challenge to computer vision research. Most of the previous scene recognition models are based on the so-called “bag of visual words” method, which uses some clustering method to quantize the numerous local region descriptors into a codebook. The size of the codebook and the selection of initial clustering center have great influence on the performance. Furthermore, the big size of the codebook has high computational cost and memory consumption. To overcome these drawbacks, we present an unsupervised natural scene recognition approach that is not based on the “bag of visual words” method. This approach works by creating multiple resolution images and partitioning them into sub-regions at different scales. The descriptors of all sub-regions in the same resolution image are directly concatenated for support vector machine (SVM) classifiers. To represent images more effectively, we present a new visual descriptor: weighted histograms of gradient orientation (WHGO). We evaluate our approach on three data sets: the 8 scene categories of Oliva et al., the 13 scene categories of Fei-Fei et al. and the 15 scene categories of Lazebnik et al. Experiments show that the WHGO descriptor outperforms the classical scale invariant feature transform (SIFT) descriptor in natural scene recognition, and our approach achieves good performances with respect to the state of the art methods.

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Author information

Authors and Affiliations

  1. College of Mechatronics and Automation, National University of Defense Technology, Changsha, 410073, China

    Li Zhou, Dewen Hu & Zongtan Zhou

  2. College of Electronic Science and Engineering, National University of Defense Technology, Changsha, 410073, China

    Zhaowen Zhuang

Authors
  1. Li Zhou
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  2. Dewen Hu
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  3. Zongtan Zhou
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  4. Zhaowen Zhuang
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Corresponding author

Correspondence to Dewen Hu.

Additional information

Li ZHOU was born in Hunan, China, in 1982. He received the B.Sc. and M.Sc. degrees from Dalian Navy Academy, China, in 2004 and 2006, respectively. He is currently working toward the doctoral degree in the National University of Defense Technology. His research interests include computer/biological vision, visual navigation, and machine learning.

Dewen HU was born in Hunan, China, in 1963. He received the B.Sc. and M.Sc. degrees from Xi’an Jiaotong University, China, in 1983 and 1986, respectively. From 1986, he was with the National University of Defense Technology. From October 1995 to October 1996, he was a Visiting Scholar with the University of Sheffield, UK. He got his Ph.D degree from the National University of Defense Technology in 1999. He was promoted Professor in 1996. His research interests include image processing, system identification and control, neural networks, and cognitive science. He is an action editor of Neural Networks.

Zongtan ZHOU was born in Henan, China, in 1969. He received the B.Sc., M.Sc. and Ph.D degrees from the National University of Defense Technology, China, in 1990, 1994 and 1998, respectively. From February 2010 to February 2011, He was a Visiting Scholar with the Eberhard Karls Universitt Tübingen. He was promoted Professor in 2007. His research interests include image/signal processing, computer/biological vision, neural networks, cognitive neuroscience and brain-computer interface.

Zhaowen ZHUANG was born in Fujian, China, in 1958. He received the B.Sc. and M.Sc. degrees from the National University of Defense Technology, China, in 1981 and 1984, and the Ph.D degree from Beijing Institute of Technology at Beijing in 1989, respectively, both in electronic engineering. He worked in Purdue University as Senior Visiting Scholar to conduct radar signal processing in 1999. He is now a Professor in the National University of Defense Technology and performs research on radar target recognition, artificial intelligence and signal processing in satellite navigation.

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Zhou, L., Hu, D., Zhou, Z. et al. Natural scene recognition using weighted histograms of gradient orientation descriptor. Front. Electr. Electron. Eng. China 6, 318–327 (2011). https://doi.org/10.1007/s11460-011-0140-4

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  • DOI: https://doi.org/10.1007/s11460-011-0140-4

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