Low memory implementation of saliency map using strip-based method

Christopher Wing Hong Ngau, Li Minn Ang, Kah Phooi Seng

Research output: Book chapter/Published conference paperConference paper

Abstract

Works in the area of visual saliency are expanding rapidly where visual salience is beginning to find importance in many multimedia and object detection applications. The core of the visual saliency models is the saliency map where information from various features such as intensity, colour, and orientation are encoded onto a single master map. However, the required amount of memory to hold the related maps in the computation of the saliency map is large. This could be seen as a potential complication in hardware constrained environment. In this paper, a low memory implementation of a saliency map using strip-based method is proposed. Simulation results showed that the strip-based method is able to provide a reasonable saliency map while keeping the memory requirements low.

Original languageEnglish
Title of host publicationVisual Informatics
Subtitle of host publicationBridging Research and Practice - First International Visual Informatics Conference, Conference, IVIC 2009, Proceedings
Pages715-726
Number of pages12
DOIs
Publication statusPublished - 01 Dec 2009
Event1st International Visual Informatics Conference, IVIC 2009 - Kuala Lumpur, Malaysia
Duration: 11 Nov 200913 Nov 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5857 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st International Visual Informatics Conference, IVIC 2009
CountryMalaysia
CityKuala Lumpur
Period11/11/0913/11/09

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    Ngau, C. W. H., Ang, L. M., & Seng, K. P. (2009). Low memory implementation of saliency map using strip-based method. In Visual Informatics: Bridging Research and Practice - First International Visual Informatics Conference, Conference, IVIC 2009, Proceedings (pp. 715-726). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5857 LNCS). https://doi.org/10.1007/978-3-642-05036-7_68