吉林大学学报(工学版) ›› 2013, Vol. 43 ›› Issue (增刊1): 450-454.

Previous Articles     Next Articles

Scene classification based on coding layer feature combination

CUI Yin1, DUAN Fei2, ZHANG Yu-jin2   

  1. 1. School of Electronic Information Engineering, Beihang University, Beijing 100191, China;
    2. Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
  • Received:2012-08-27 Published:2013-06-01

Abstract:

In order to improve classification performance of image,one efficient method was to combine different features together.The combination in various layers (feature layer and coding layer) was studied with the help of SIFT feature and normalized color histogram (NCH) feature,their effects were also compared.Experiments on several popular datasets show that,compared to combined features,the feature combination after feature extraction can reduce feature dimension while keep the similar classification rate,and the feature combination on coding layer can achieve even higher classification rate.It is concluded that feature combination should be better performed on higher semantic level.

Key words: scene classification, bag of words, feature combination, semantic level

CLC Number: 

  • TN911.73

[1] 章毓晋.图像工程(下册):图像理解[M].(第3版).北京:清华大学出版社,2012.

[2] Oliva A,Torralba A.Modeling the shape of the scene:a holistic representation of the spatial envelope [J].IJCV,2001,42(3):145-175.

[3] Sivic J,Zisserman A.Video Google:A text retrieval approach to object matching in videos [C]//Proc ICCV II,Nice,France,2003:1470-1477.

[4] Li F F,Perona P.A bayesian hierarchical model for learning natural scene categories [C]//Proc CVPR,San Diego,USA,2005:524-531.

[5] Bosch A,Zisserman A,Munoz X.Scene classi cation via pLSA [C]//Proc ECCV,Graz, Austria,2006:517-530.

[6] Blei D,Ng A,Jordan M.Latent dirichlet allocation [J].Journal of Machine Learning Research,2003(3):993-1022.

[7] Sivic J,Russell B C,Efros A A,et al.Discovering objects and their location in images [C]//Proc ICCV,Beijing,China,2005:370-377.

[8] Lazebnik S,Schmid C,Ponce J.Beyond bags of features:Spatial pyramid matching for recognizing natural scene categories [C]//Proc CVPR,New York,2006:2169-2178.

[9] Yang J,Yu K,Gong Y,et al.Linear spatial pyramid matching using sparse coding for image classification [C]//Proc CVPR,2009:1794-1801.

[10] Lowe D.Distinctive image features from scale-invariant key points [J].IJCV,2004,60(2):91-110.

[11] Nixon M S,Aguado A S.Feature extraction and image processing [M].(2ed).Academic Press,2008.

[12] Van de Sande K E A,Gevers T,Snoek C G M.Evaluation of color descriptors for objects and scene recognition [C]// Proc CVPR,Anchorage,USA,2008:1-8.

[13] Van Gemert J C,Veenman C J,Smeulders A W M,et al.Visual word ambiguity [J].TPAMI,2010,32(7):1271-1283.

[14] Boureau Y,Bach F,Le Cun Y,et al.Learning mid-level features for recognition [C]//Proc CVPR,2010:2559-2566.

[15] Maji S,Berg A C,Malik J.Classification using intersection kernel support vector machine is efficient [C]//Proc CVPR,Anchorage,USA,2008:1-8.

[1] ZHAO Hong-wei, LI Qing-liang, LIU Ping-ping, TANG Huan-yu. Feature saliency constraint based image retrieval method [J]. 吉林大学学报(工学版), 2016, 46(2): 542-548.
[2] ZHAO Hong-wei, LI Qing-liang, TANG Huan-yu, ZANG Xue-bai. Spatial verification method based on local regional constraint [J]. 吉林大学学报(工学版), 2016, 46(1): 265-270.
[3] JI Chao, LIU Hui-ying, SUN Jing-feng, HE Sheng, HUANG Min-zhu. Image salient region detection based on spatial and frequency domains [J]. 吉林大学学报(工学版), 2014, 44(01): 177-183.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!