Computer Science ›› 2014, Vol. 41 ›› Issue (8): 281-285.doi: 10.11896/j.issn.1002-137X.2014.08.059

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Research on Extended BoF Model

LIANG Ye,LIU Hong-zhe and YU Jian   

  • Online:2018-11-14 Published:2018-11-14

Abstract: BoF feature is one of the most popular image representation methods by now.Aiming at the weaknesses of hard assignment coding and discarding spatial information,the improvements of feature coding and pooling in traditional BoF paradigm were proposed.The new image representation can be used for image classification.First,multi-annulus partition method was proposed for feature pooling,which can be embedded more spatial information.Second,multi-words hard assignment coding method was proposed according to long-tail distribution of dense samples and relatively even distribution of features in scene images.The new representation not only preserves merits of BoF paradigm but also is more compact and has more spatial information.The experimental results prove the efficiency of the new method.

Key words: BoF,Feature quantization,Feature pooling,Image representation,Image classification

[1] Csurka G,Dance C R,Fan Li-xin,et al.Visual categorizationwith bags of keypoints[C]∥Proceedings of European Conference Computer Vision 2004,workshop on Statistical Learning in Computer Vision.Prague,Czech Republic:Springer-Verlag LNCS,2004:59-74
[2] Coates A,Ng A Y.The importance of encodingversus trainingwith sparse coding and vector quantization[C]∥ICMA.2011
[3] Rigamonti R,Brown M A,Lepetit V.Are sparse representations really relevant for image classification[C]∥CVPR.2011
[4] Sivic J,Zisserman A.Video google:A Text Retrieval Approach to Object Matching in Videos[C]∥Proceedings of IEEE International Conference on Computer Vision.Nice,France:IEEE Computer Society,2003:1470-1477
[5] Lee H,Battle A,Raina R,et al.Efficient sparse coding algo-rithms[C]∥Proceedings of Advances in Neural Information Processing System,2006.2006:801-808
[6] Yang J,Yu K,Gong Y,et al.Linear Spatial Pyramid Matching Using Sparse Coding for Image Classification[C]∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Miami,Florida,USA:IEEE Computer Society,2009:1794-1801
[7] Gao S,Tsang I,Chia L,et al.Local Features Are Not Lonely-Laplacian Sparse Coding for Image Classification[C]∥Procee-dings of IEEE Conference on Computer Vision and Pattern Re-cognition.San Francisco,CA,USA:IEEE Computer Society,2010:3555-3561
[8] Wang J,Yang J,Yu K,et al.Locality-constrained linear coding for image classification[C]∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.San Francisco,CA,USA:IEEE Computer Society,2010:3360-3367
[9] Yu K,Zhang T,Gong Y.Nonlinear Learning Using Local Coordinate Coding[C]∥Proceedings of Advances in Neural Information Processing System,2009.Vancouver,British Columbia,Canada:Springer,2009
[10] Liu Ling-qiao,Wang Lei,Liu Xin-wang.In Defense of Soft-as-signment Coding[C]∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition,2011.Colorado Springs,CO,USA:IEEE Computer Society,2011:2486-2493
[11] Huang Y,Huang K,Yu Y,et al.Salient Coding for Image Classification[C]∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition,2011.Colorado Springs,CO,USA:IEEE Computer Society,2011:1753-1760
[12] Lazebnik S,Schmid C,Ponce J.Beyond bags of features:Spatial pyramid matching for recognizing natural scene categories[C]∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition,2006.New York,NY,USA:IEEE Computer Society,2006:2169-2178
[13] Avila S,Thome N,Cord M,et al.Bossa:Extended Bow Formali-sm for Image Classification[C]∥Proceedings of International Conference on Image Processing,2011.Brussels,Belgium:IEEE Computer Society,2011:2909-2912
[14] Harada T,Ushiku Y,Yamashita Y,et al.Discriminative Spatial Pyramid[C]∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition,2011.Colorado Springs,CO,USA:IEEE Computer Society,2011:1617-1624
[15] Cao Yang,Wang Chang-hu,Li Zhi-wei,et al.Spatial-Bag-of-Features[C]∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition,2010.San Francisco,CA,USA:IEEE Computer Society,2010:3352-3359
[16] Malinowski M,Fritz M.Learnable Pooling Regions for ImageClassification[C]∥Proceedings of CoRR.2013
[17] Jia Yang-qing,Huang Chang,Darrell T.Beyond Spatial Pyra-mids:Receptive Field Learning for Pooled Image Features[C]∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition,2012.Providence,RI,USA:IEEE Computer Society,2012:3370-3377
[18] McCann S,Lowe D G.Local Naive Bayes Nearest Neighbor for image classification[C]∥IEEE Conference on Computer Vision and Pattern Recognition.2012:3650-3656
[19] Lowe D.Distinctive Image Features from Scale-invariant Key-points[J].International Journal of Computer Vision,2004,0(2):91-110
[20] Van De Sande,Gevers K E A T,Snoek C G M.Evaluating colordescriptors for object and scene recognition[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2010,32:1582-1596
[21] Hui Bin,Tang Xu-sheng,Luo Hai-bo,et al.SDF Matched Filter Based on Gabor Wavelet Transform for Face Recognition[J].Information and Control,2008,37(5):633-636

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