liu.seSearch for publications in DiVA
Change search
ReferencesLink to record
Permanent link

Direct link
Saliency Maps using Channel Representations
Linköping University, Department of Electrical Engineering, Computer Vision.
2010 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesisAlternative title
Saliency-kartor utifrån kanalrepresentationer (Swedish)
Abstract [en]

In this thesis an algorithm for producing saliency maps as well as an algorithm for detecting salient regions based on the saliency map was developed. The saliency values are computed as center-surround differences and a local descriptor called the region p-channel is used to represent center and surround respectively. An integral image representation called the integral p-channel is used to speed up extraction of the local descriptor for any given image region. The center-surround difference is calculated as either histogram or p-channel dissimilarities.

Ground truth was collected using human subjects and the algorithm’s ability to detect salient regions was evaluated against this ground truth. The algorithm was also compared to another saliency algorithm.

Two different center-surround interpretations are tested, as well as several p-channel and histogram dissimilarity measures. The results show that for all tested settings the best performing dissimilarity measure is the so called diffusion distance. The performance comparison showed that the algorithm developed in this thesis outperforms the algorithm against which it was compared, both with respect to region detection and saliency ranking of regions. It can be concluded that the algorithm shows promising results and further investigation of the algorithm is recommended. A list of suggested approaches for further research is provided.

Place, publisher, year, edition, pages
2010. , 62 p.
Keyword [en]
computer vision, saliency maps, p-channels
National Category
Computer Vision and Robotics (Autonomous Systems)
URN: urn:nbn:se:liu:diva-53734ISRN: LITH-ISY-EX--10/4169--SEOAI: diva2:291472
Subject / course
Computer Vision Laboratory
2010-01-28, Algoritmen, B-huset, Campus Valla, Linköpings universitet, Linköping, 13:15 (Swedish)
Available from: 2010-02-05 Created: 2010-02-01 Last updated: 2012-05-30Bibliographically approved

Open Access in DiVA

fulltext(2234 kB)197 downloads
File information
File name FULLTEXT01.pdfFile size 2234 kBChecksum SHA-512
Type fulltextMimetype application/pdf

By organisation
Computer Vision
Computer Vision and Robotics (Autonomous Systems)

Search outside of DiVA

GoogleGoogle Scholar
Total: 197 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 247 hits
ReferencesLink to record
Permanent link

Direct link