Image modeling using statistical measures for visual object categorization

Since the challenging visual object categorization has attracted more and more attention in recent years, we present in this paper a novel approach called statistical measures based image modeling for this problem, thus avoiding the major difficulty of the popular “bag-of-visual words” approach which needs to fix a visual vocabulary size. We use a series of statistical measures over our proper region based color and segment features as well as the popular SIFT, extracted from an image, to model its visual content. Then this new image modeling will be fed to a certain classifier to accomplish the object categorization task. Several classification schemes combined with some feature selection techniques and fusion strategies have also been implemented and compared within the experimentation carried out on a subset of Pascal VOC dataset. The results show that merging the region based features and SIFT which are from different sources using an early fusion can actually improve classification performance, suggesting that these features managed to extract information which is complementary to each other.

[1]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[2]  Yoav Freund,et al.  A Short Introduction to Boosting , 1999 .

[3]  Karl Pearson F.R.S..  LIII. On lines and planes of closest fit to systems of points in space , 1901 .

[4]  M. Wertheimer.  Untersuchungen zur Lehre von der Gestalt. II , 1923 .

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

[6]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[7]  Ian T. Jolliffe,et al.  Principal Component Analysis , 2002, International Encyclopedia of Statistical Science.

[8]  Pietro Perona,et al.  A Bayesian hierarchical model for learning natural scene categories , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[9]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[10]  R. Fisher.  THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[11]  Pedro Larrañaga,et al.  A review of feature selection techniques in bioinformatics , 2007, Bioinform..

[12]  Markus A. Stricker,et al.  Similarity of color images , 1995, Electronic Imaging.

[13]  E. M. Wright,et al.  Adaptive Control Processes: A Guided Tour , 1961, The Mathematical Gazette.

[14]  Liming Chen,et al.  Line segment based edge feature using Hough transform , 2007 .

[15]  B. S. Manjunath,et al.  An efficient color representation for image retrieval , 2001, IEEE Trans. Image Process..

[16]  Linlin Shen,et al.  AdaBoost Gabor Feature Selection for Classification , 2004 .

[17]  Chih-Jen Lin,et al.  Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel , 2003, Neural Computation.

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

[19]  Gabriela Csurka,et al.  Visual categorization with bags of keypoints , 2002, eccv 2004.

[20]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[21]  Hsuan-Tien Lin.  A Study on Sigmoid Kernels for SVM and the Training of non-PSD Kernels by SMO-type Methods , 2005 .

[22]  L. Chen,et al.  Coarse adaptive color image segmentation for visual object classification , 2008, 2008 15th International Conference on Systems, Signals and Image Processing.

[23]  Cordelia Schmid,et al.  Scale & Affine Invariant Interest Point Detectors , 2004, International Journal of Computer Vision.

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

[25]  Gabriela Csurka,et al.  Adapted Vocabularies for Generic Visual Categorization , 2006, ECCV.