PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A hybrid supervised-unsupervised visual vocabulary algorithm for concept recognition
Alexander Binder, Wojciech Wojcikiewicz, Christina Mueller and Motoaki Kawanabe
In: Computer Vision - ACCV 2010, Part III Lecture Notes in Computer Science , 6494 . (2011) Springer , Heidelberg, Germany , pp. 95-108.


Vocabulary generation is the essential step in the bag-of-words image representation for visual concept recognition, because its quality affects classification performance substantially. In this paper, we propose a hybrid method for visual word generation which combines unsupervised density-based clustering with the discriminative power of fast support vector machines. We aim at three goals: breaking the vocabulary generation algorithm up into two sections, with one highly parallelizable part, reducing computation times for bag of words features and keeping concept recognition performance at levels comparable to vanilla k-means clustering. On the two recent data sets Pascal VOC2009 and ImageCLEF2010 PhotoAnnotation, our proposed method either outperforms various baseline algorithms for visual word generation with almost same computation time or reduces training/test time with on par classification performance.

EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Learning/Statistics & Optimisation
Information Retrieval & Textual Information Access
ID Code:8012
Deposited By:Alexander Binder
Deposited On:17 March 2011