Grouping and summarizing scene images from web collections

Heng Yang, Qing Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

This paper presents an efficient approach to group and summarize the large-scale image dataset gathered from the internet. Our method firstly employs the bag-of-visual-words model which has been successfully used in image retrieval applications to give the similarity between images and divides the large image collections into separated coarse groups. Next, in each group, we match the features between each pair of images by using an area ratio constraint which is an affine invariant. The number of matched features is taken as the new similarity between images, by which the initial grouping results are refined. Finally, one canonical image for one group is chosen as the summarization. The proposed approach is tested on two datasets consisting of thousands of images which are collected from the photo-sharing website. The experimental results demonstrate the efficiency and effectiveness of our method.

Original languageEnglish
Title of host publicationAdvances in Visual Computing - 5th International Symposium, ISVC 2009, Proceedings
Pages315-324
Number of pages10
EditionPART 2
DOIs
StatePublished - 2009
Event5th International Symposium on Advances in Visual Computing, ISVC 2009 - Las Vegas, NV, United States
Duration: 30 Nov 20092 Dec 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume5876 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th International Symposium on Advances in Visual Computing, ISVC 2009
Country/TerritoryUnited States
CityLas Vegas, NV
Period30/11/092/12/09

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