6533b82dfe1ef96bd129145e

RESEARCH PRODUCT

Visual saliency by keypoints distribution analysis

Giuseppe MazzolaEdoardo ArdizzoneAlessandro Bruno

subject

saliency visual attentiontexture SIFTComputer sciencebusiness.industryFixation (visual)ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONVisual attentionScale-invariant feature transformPattern recognitionComputer visionTop-down and bottom-up designArtificial intelligencebusinessVisual saliency

description

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.

yearjournalcountryeditionlanguage
2011-01-01
http://link.springer.com/chapter/10.1007/978-3-642-24085-0_70