Visual words selection based on class separation measures

Bag of Visual Words is one of the most effective image representations. One of the optimization methods for BoVW is the selection of the most informative visual words, which leads to more compact visual dictionaries and more accurate categorization. In this paper we investigate the problem of feature selection in the Bag of Visual Words framework. The main contribution is the presentation of two novel methods for visual word selection. The first one choses the features which are the best at separating one class from the rest (MFM1 one-vs-all). In the second method, the features which are the best at separating class pairs are selected (MSF6 one-vs-one). The effectiveness of the proposed methods is verified empirically on two different image datasets.

[1]  Gary R. Bradski,et al.  ORB: An efficient alternative to SIFT or SURF , 2011, 2011 International Conference on Computer Vision.

[2]  Piotr Artiemjew,et al.  The Extraction Method of DNA Microarray Features Based on Modified F Statistics vs. Classifier Based on Rough Mereology , 2011, ISMIS.

[3]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[4]  Shawn D. Newsam,et al.  Spatial pyramid co-occurrence for image classification , 2011, 2011 International Conference on Computer Vision.

[5]  Jianguo Zhang,et al.  The PASCAL Visual Object Classes Challenge , 2006 .

[6]  Cordelia Schmid,et al.  Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study , 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).

[7]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[8]  Pietro Perona,et al.  Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[9]  Piotr Artiemjew,et al.  The Extraction Method of DNA Microarray Features Based on Experimental A Statistics , 2011, RSKT.

[10]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[11]  Piotr Artiemjew,et al.  DNA microarray classification by means of weighted voting based on rough set classifier , 2010, 2010 International Conference of Soft Computing and Pattern Recognition.

[12]  Pietro Perona,et al.  Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[13]  C. V. Jawahar,et al.  Cats and dogs , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[15]  Chong-Wah Ngo,et al.  Representations of Keypoint-Based Semantic Concept Detection: A Comprehensive Study , 2010, IEEE Transactions on Multimedia.

[16]  Andrew Zisserman,et al.  Automated Flower Classification over a Large Number of Classes , 2008, 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing.

[17]  Michael Isard,et al.  Object retrieval with large vocabularies and fast spatial matching , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[18]  Piotr Artiemjew,et al.  Review of the Extraction Methods of DNA Microarray Features Based on Central Decision Class Separation vs Rough Set Classifier , 2012 .

[19]  G. A. White,et al.  Mechanisms of injury and emergency care of acute spinal cord injury in dogs and cats. , 2012, Journal of veterinary emergency and critical care.

[20]  Shusaku Tsumoto,et al.  Foundations of Intelligent Systems , 2003, Lecture Notes in Computer Science.

[21]  Cordelia Schmid,et al.  Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[22]  Yiming Yang,et al.  A Comparative Study on Feature Selection in Text Categorization , 1997, ICML.

[23]  Krzysztof Sopyla,et al.  Ranking by K-Means Voting Algorithm for Similar Image Retrieval , 2012, ICAISC.

[24]  Chong-Wah Ngo,et al.  Evaluating bag-of-visual-words representations in scene classification , 2007, MIR '07.

[25]  Vincent Lepetit,et al.  BRIEF: Binary Robust Independent Elementary Features , 2010, ECCV.

[26]  Andrew Zisserman,et al.  A Visual Vocabulary for Flower Classification , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[27]  Piotr Artiemjew,et al.  Classifiers based on rough mereology in analysis of DNA microarray data , 2010, 2010 International Conference of Soft Computing and Pattern Recognition.