An algorithm for detecting multiple salient objects in images via adaptive feature selection

This paper presents an algorithm for detecting multiple salient objects in images. The algorithm extends our previous algorithm which was designed to detect only a single salient object. The new algorithm employs five feature maps (lightness distance, color distance, contrast, sharpness, and edge st...

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Bibliographic Details
Published in2012 19th IEEE International Conference on Image Processing pp. 657 - 660
Main Authors Vu, C. T., Chandler, D. M.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2012
Subjects
adaptive feature selection
Algorithm design and analysis
Clustering algorithms
Feature extraction
Image color analysis
Image edge detection
Image segmentation
main subject detection
Salient
salient object detection
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Summary:This paper presents an algorithm for detecting multiple salient objects in images. The algorithm extends our previous algorithm which was designed to detect only a single salient object. The new algorithm employs five feature maps (lightness distance, color distance, contrast, sharpness, and edge strength), along with a new image-adaptive technique for estimating the usefulness of each feature map based on a local measure of cluster density. As we will demonstrate, our new version can successfully detect multiple salient objects on images for which the previous version did not succeed. Testing on subsets of images from two databases shows that the proposed algorithm performs well on a variety of images containing multiple salient objects.
ISBN:1467325341
9781467325349
ISSN:1522-4880
DOI:10.1109/ICIP.2012.6466945