Improving Image Classification through Descriptor Combination

The efficiency in image classification tasks can be improved using combined information provided by several sources, such as shape, color, and texture visual properties. Although many works proposed to combine different feature vectors, we model the descriptor combination as an optimization problem to be addressed by evolutionary-based techniques, which compute distances between samples that maximize their separability in the feature space. The robustness of the proposed technique is assessed by the Optimum-Path Forest classifier. Experiments showed that the proposed methodology can outperform individual information provided by single descriptors in well-known public datasets.

[1]  Zong Woo Geem,et al.  Music-Inspired Harmony Search Algorithm , 2009 .

[2]  Alexandre X. Falcão,et al.  Multimodal Pattern Recognition Through Particle Swarm Optimization , 2010 .

[3]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[4]  Edward A. Fox,et al.  A genetic programming framework for content-based image retrieval , 2009, Pattern Recognit..

[5]  Ricardo da Silva Torres,et al.  Comparative study of global color and texture descriptors for web image retrieval , 2012, J. Vis. Commun. Image Represent..

[6]  Dingxing Wang,et al.  Boosting image classification with LDA-based feature combination for digital photograph management , 2005, Pattern Recognit..

[7]  Z. Geem Music-Inspired Harmony Search Algorithm: Theory and Applications , 2009 .

[8]  Sebastian Nowozin,et al.  On feature combination for multiclass object classification , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[9]  Jun'ichi Tsujii,et al.  Learning Combination Features with L1 Regularization , 2009, HLT-NAACL.

[10]  Yong Yang,et al.  Evaluating Feature Combination in Object Classification , 2011, ISVC.

[11]  Jorge Stolfi,et al.  The image foresting transform: theory, algorithms, and applications , 2004 .

[12]  Ricardo da Silva Torres,et al.  Content-Based Image Retrieval: Theory and Applications , 2006, RITA.

[13]  João Paulo Papa,et al.  Supervised pattern classification based on optimum‐path forest , 2009, Int. J. Imaging Syst. Technol..

[14]  Yan Fu,et al.  Image classification based on multi-feature combination and PCA-RBaggSVM , 2010, 2010 IEEE International Conference on Progress in Informatics and Computing.

[15]  João Paulo Papa,et al.  Efficient supervised optimum-path forest classification for large datasets , 2012, Pattern Recognit..