Visual saliency by keypoints distribution analysis
Authors: Ardizzone, E., Bruno, A. and Mazzola, G.
Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume: 6978 LNCS
Issue: PART 1
Pages: 691-699
eISSN: 1611-3349
ISBN: 9783642240843
ISSN: 0302-9743
DOI: 10.1007/978-3-642-24085-0_70
Abstract:In this paper we introduce a new method for Visual Saliency detection. The goal of our method is to emphasize regions that show rare visual aspects in comparison with those showing frequent ones. We propose a bottom up approach that performs a new technique based on low level image features (texture) analysis. More precisely, we use SIFT Density Maps (SDM), to study the distribution of keypoints into the image with different scales of observation, and its relationship with real fixation points. The hypothesis is that the image regions that show a larger distance from the mode (most frequent value) of the keypoints distribution over all the image are the same that better capture our visual attention. Results have been compared to two other low-level approaches and a supervised method. © 2011 Springer-Verlag.
Source: Scopus
Visual saliency by keypoints distribution analysis
Authors: Ardizzone, E., Bruno, A. and Mazzola, G.
Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume: 6978 LNCS
Pages: 691-699
eISSN: 1611-3349
ISBN: 9783642240843
ISSN: 0302-9743
DOI: 10.1007/978-3-642-24085-0_70
Abstract:In this paper we introduce a new method for Visual Saliency detection. The goal of our method is to emphasize regions that show rare visual aspects in comparison with those showing frequent ones. We propose a bottom up approach that performs a new technique based on low level image features (texture) analysis. More precisely, we use SIFT Density Maps (SDM), to study the distribution of keypoints into the image with different scales of observation, and its relationship with real fixation points. The hypothesis is that the image regions that show a larger distance from the mode (most frequent value) of the keypoints distribution over all the image are the same that better capture our visual attention. Results have been compared to two other low-level approaches and a supervised method.
Source: Scopus
Visual Saliency by Keypoints Distribution Analysis
Authors: Ardizzone, E., Bruno, A. and Mazzola, G.
Journal: IMAGE ANALYSIS AND PROCESSING - ICIAP 2011, PT I
Volume: 6978
Pages: 691-699
eISSN: 1611-3349
ISSN: 0302-9743
Source: Web of Science (Lite)
Visual saliency by keypoints distribution analysis
Authors: Ardizzone, E., Bruno, A. and Mazzola, G.
Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume: 6978 LNCS
Pages: 691-699
DOI: 10.1007/978-3-642-24085-0_70
Source: Manual