Semantic feature selection for object discovery in high-resolution remote sensing imagery

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

7 Scopus citations

Abstract

Given its importance, the problem of object discovery in High-Resolution Remote-Sensing (HRRS) imagery has been given a lot of attention by image retrieval researchers. Despite the vast amount of expert endeavor spent on this problem, more effort has been expected to discover and utilize hidden semantics of images for image retrieval. To this end, in this paper, we exploit a hyperclique pattern discovery method to find complex objects that consist of several co-existing individual objects that usually form a unique semantic concept. We consider the identified groups of co-existing objects as new feature sets and feed them into the learning model for better performance of image retrieval. Experiments with real-world datasets show that, with new semantic features as starting points, we can improve the performance of object discovery in terms of various external criteria.

Original languageEnglish (US)
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 11th Pacific-Asia Conference, PAKDD 2007, Proceedings
PublisherSpringer Verlag
Pages71-83
Number of pages13
ISBN (Print)9783540717003
DOIs
StatePublished - 2007
Event11th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2007 - Nanjing, China
Duration: May 22 2007May 25 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4426 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other11th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2007
Country/TerritoryChina
CityNanjing
Period5/22/075/25/07

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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