Large-scale image categorization is a challenging task. In this paper, we propose a new image categorization approach based on visual saliency and bag-of-words model. Firstly, a saliency map is generated by visual saliency method that exploits some coarsely localized information, i.e. the salient region shape and contour. Secondly, size of salient region is acquired by calculating maximum entropy. Thirdly, the local image descriptor-SIFT extracted in the salient region and visual saliency information are combined to build visual words. Finally, the visual word bag is categorized by Support Vector Machine. By comparing with BOW model categorization methods, experiment results show that our methods can effectively improve the accuracy of image categorization.
A fusion algorithm of infrared and visible images based on visual saliency map (VSM) and nonsubsampled contourlet transform (NSCT) was proposed. Usually, the visual salient region of infrared image is directed towards the targets which interpret the most important information in the image. For the given registered infrared and visible images, firstly, the frequency-tuned (FT) saliency detection algorithm is used to calculate the visual saliency map of infrared and visible images. Then the size of each salient region is obtained by maximizing entropy. In order to capture the details of the infrared and visible images, the low and high frequency fusion coefficients of nonsubsampled contourlet transform (NSCT) are selected based on region saliency, region energy (RE) and region sharpness (RS). Four different data sets from TNO, Human Factors are employed, and experimental results indicate that the proposed method can achieve a superior performance compared with other fusion methods in both subjective and objective assessments.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.