Towards Effective Image Classification Using Class-Specific Codebooks and Distinctive Local Features

Date

2015-03-01

Author

Altintakan, Umit Lutfu
Yazıcı, Adnan

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Local image features, which are robust to scale, view, and orientation changes in images, play a key factor in developing effective visual classification systems. However, there are two main limitations to exploit these features in image classification problems: 1) a large number of key-points are located during the feature detection process, and 2) most of the key-points arise in background regions, which do not contribute to the classification process. In order to decrease the inverse effects of these limitations, we propose a new codebook generation approach through employing a new clustering method that generates class-specific codebooks along with a novel feature selection method in the bag-of-words model. We evaluate the performance of different classification techniques including Naive Bayesian, k-NN, and SVM on distinctive features. Experiments conducted on PASCAL Visual Object Classification collections have shown that the class-specific codebooks along with distinctive image features can significantly improve the classification performances.

Subject Keywords

Bag-of-words, Class-specific codebooks, Distinctive local features, Image classification, Self-organizing maps

URI

https://hdl.handle.net/11511/35664

Journal

IEEE TRANSACTIONS ON MULTIMEDIA

DOI

https://doi.org/10.1109/tmm.2014.2388312

Collections

Department of Computer Engineering, Article

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Citation Formats

U. L. Altintakan and A. Yazıcı, “Towards Effective Image Classification Using Class-Specific Codebooks and Distinctive Local Features,” IEEE TRANSACTIONS ON MULTIMEDIA, pp. 323–332, 2015, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/35664.