By Topic

Hierarchical classifier with multiple feature weighted fusion for scene recognition

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Kikutani, Y. ; Grad. Sch. of Sci. & Eng., Ritsumeikan Univ., Kusatsu, Japan ; Okamoto, A. ; Xian-Hua Han ; Xiang Ruan
more authors

Recently, scene recognition is becoming an additional function in digital camera. Automatic scene understanding is a highest-level operation in computer vision, and it is a very difficult and largely unsolved problem. The conventional methods usually use global features (such as color histogram, texture, edge) for image representation and recognize scene types with some classifiers (such as Bayesian, Neural Network, Support Vector Machine and so on). However, the recognition rate still cannot satisfy the requirement of real applications. In this paper, we proposed to use weighted fusion of global feature (Color histogram) and local feature (Bag-Of-Feature model) for scene image representation, and use hierarchical classifier according the visual feature properties of scene types for scene recognition. Experimental results show that the recognition rate with our proposed algorithm can be improved compared to the state of art algorithms.

Published in:

Software Engineering and Data Mining (SEDM), 2010 2nd International Conference on

Date of Conference:

23-25 June 2010