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

Altintakan U. L., YAZICI A.

IEEE TRANSACTIONS ON MULTIMEDIA, cilt.17, sa.3, ss.323-332, 2015 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 17 Sayı: 3
  • Basım Tarihi: 2015
  • Doi Numarası: 10.1109/tmm.2014.2388312
  • Dergi Adı: IEEE TRANSACTIONS ON MULTIMEDIA
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.323-332
  • Anahtar Kelimeler: Bag-of-words, class-specific codebooks, distinctive local features, image classification, self-organizing maps, UNIVERSAL
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

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.