A novel image retrieval framework exploring inter cluster distance

Xin Xin*, Aggelos K Katsaggelos

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

An image could be described with local features like SIFT and with those features, images could be represented as "Bag-of-Visual-Words"(BVW). This representation has been widely used in content based image retrieval. Comparing BVW of two images is usually done in Euclidean space, like Euclidean distance or weighted variants. Neither of these methods consider the inter cluster relations. If there is a feature in one image without any match in all the clusters of another image's features, there will be no score for that feature. But, there are still some match in neighbor clusters. In this paper, we use dynamic programming to calculate full inter cluster distance map and with the distance, we can evaluate a feature in neighbor clusters. Our proposed method is evaluated in Caltech 101 database and experiments show that our method generally exceeds the method that don't consider inter cluster distance.

Original languageEnglish (US)
Title of host publication2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings
Pages3213-3216
Number of pages4
DOIs
StatePublished - Dec 1 2010
Event2010 17th IEEE International Conference on Image Processing, ICIP 2010 - Hong Kong, Hong Kong
Duration: Sep 26 2010Sep 29 2010

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Other

Other2010 17th IEEE International Conference on Image Processing, ICIP 2010
CountryHong Kong
CityHong Kong
Period9/26/109/29/10

Keywords

  • Bag of visual word
  • Dynamic programming
  • ISOMAP
  • Image retrieval

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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