Visual information mining and ranking using graded relevance assessments in satellite image databases

Adrian S. Barb, Chi Ren Shyu

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

2 Scopus citations

Abstract

With recent technological advances, the geospatial industry produces digital image data at an astonishing rate. Such large amounts of data need to be analyzed for visual content in a timely fashion. For in-depth analysis of the geospatial there is a need to find efficient methods to process the visual information into actionable knowledge. One of the most promising methods is to evaluate the relevance of geospatial images to domain-specific visual semantics. Most of existing methods for annotating semantic meaning to geospatial images are trained using binary feedback from users. Such approaches may lead to suboptimal models especially due to the fact that semantic relevance of images is rarely a binary problem. In this paper, we report an algorithm to link low-level image features with high-level visual semantics using graded relevance feedback from image analysts. This linkage is done using flexible possibility functions that mathematically model the existence of visual semantics in new images added to the database. Our experimental results show that our technique improves the knowledge discovery process as evidenced by increased mean average precision of semantic queries.

Original languageEnglish (US)
Title of host publication2010 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2010
Pages3398-3401
Number of pages4
DOIs
StatePublished - Dec 1 2010
Event2010 30th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2010 - Honolulu, HI, United States
Duration: Jul 25 2010Jul 30 2010

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)

Other

Other2010 30th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2010
CountryUnited States
CityHonolulu, HI
Period7/25/107/30/10

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Earth and Planetary Sciences(all)

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  • Cite this

    Barb, A. S., & Shyu, C. R. (2010). Visual information mining and ranking using graded relevance assessments in satellite image databases. In 2010 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2010 (pp. 3398-3401). [5650173] (International Geoscience and Remote Sensing Symposium (IGARSS)). https://doi.org/10.1109/IGARSS.2010.5650173