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Exploiting Visual Saliency Algorithms for Object-Based Attention: A New Color and Scale-Based Approach.

Ardizzone, E., Bruno, A. and Gugliuzza, F., 2017. Exploiting Visual Saliency Algorithms for Object-Based Attention: A New Color and Scale-Based Approach. Lecture Notes in Computer Science, 10485, 191 - 201.

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Official URL: https://www.springer.com/gb/computer-science/lncs

DOI: 10.1007/978-3-319-68548-9_18

Abstract

Visual Saliency aims to detect the most important regions of an image from a perceptual point of view. More in detail, the goal of Visual Saliency is to build a Saliency Map revealing the salient subset of a given image by analyzing bottom-up and top-down factors of Visual Attention. In this paper we proposed a new method for Saliency detection based on colour and scale analysis, extending our previous work based on SIFT spatial density inspection. We conducted several experiments to study the relationships between saliency methods and the object attention processes and we collected experimental data by tracking the eye movements of thirty viewers in the first three seconds of observation of several images. More precisely, we used a dataset that consists of images with an object in the foreground on an homogeneous background. We are interested in studying the performance of our saliency method with respect to the real fixation maps collected during the experiments. We compared the performances of our method with several state of the art methods with very encouraging result

Item Type:Article
ISSN:0302-9743
Uncontrolled Keywords:Visual saliency; object-based attention; SIFT; fixation maps; dataset; eye tracking
Group:Faculty of Media & Communication
ID Code:34242
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:03 Jul 2020 13:24
Last Modified:03 Jul 2020 13:24

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