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

https://www.scopus.com/inward/record.uri?eid=2-s2.0-80053017803&doi=10.1007%2f978-3-642-24085-0_70&partnerID=40&md5=03c2e3c1bef496d553881480371a372f

Source: Manual