Example-Specific Density Based Matching Kernels for Scene Classification Using Support Vector Machines

In this paper, we propose the example-specific density based matching kernel (ESDMK) for classification of scene images represented as sets of local feature vectors. The proposed kernel is computed between the pair of examples, represented as sets of local feature vectors, by matching the estimates of example-specific densities computed at every local feature vector in those two examples. In this work, the number of local feature vectors of an example among the K nearest neighbors of a local feature vector is considered as an estimate of the example-specific density. The minimum of the two example-specific densities, one for each example, at a local feature vector is considered as the matching score. The ESDMK is then computed as the sum of the matching score computed at every local feature vector in a pair of examples. We also propose the spatial ESDMK (SESDMK) to include spatial information present in the scene images while matching the pair of scene images. Each of the scene images is divided spatially into a fixed number of regions. Then the SESDMK is computed as a combination of region specific ESDMKs that match the corresponding regions. We study the performance of the support vector machine (SVM) based classifiers using the proposed ESDMKs for scene classification and compare with that of the SVM-based classifiers using the state-of-the-art kernels for sets of local feature vectors.

[1]  Douglas E. Sturim,et al.  Support vector machines using GMM supervectors for speaker verification , 2006, IEEE Signal Processing Letters.

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

[3]  K. Mele,et al.  Local probabilistic descriptors for image categorisation , 2009 .

[4]  Florent Perronnin,et al.  Universal and Adapted Vocabularies for Generic Visual Categorization , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Nello Cristianini,et al.  Kernel Methods for Pattern Analysis , 2004 .

[6]  Steve Renals,et al.  Evaluation of kernel methods for speaker verification and identification , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[7]  Lawrence K. Saul,et al.  Large Margin Gaussian Mixture Modeling for Phonetic Classification and Recognition , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[8]  Antonio Torralba,et al.  Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope , 2001, International Journal of Computer Vision.

[9]  Bernt Schiele,et al.  International Journal of Computer Vision manuscript No. (will be inserted by the editor) Semantic Modeling of Natural Scenes for Content-Based Image Retrieval , 2022 .

[10]  Chellu Chandra Sekhar,et al.  GMM-Based Intermediate Matching Kernel for Classification of Varying Length Patterns of Long Duration Speech Using Support Vector Machines , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[11]  Chellu Chandra Sekhar,et al.  Scene Categorization Using Large Margin Gaussian Mixture Models , 2010, IPCV.

[12]  David A. Forsyth,et al.  Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary , 2002, ECCV.

[13]  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).

[14]  Haizhou Li,et al.  A GMM supervector Kernel with the Bhattacharyya distance for SVM based speaker recognition , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[15]  Cor J. Veenman,et al.  Visual Word Ambiguity , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  N. Boujemaa,et al.  The intermediate matching kernel for image local features , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..