Perceptual feature selection for semantic image classification

Dejan Depalov*, Thrasyvoulos N. Pappas, Dongge Li, Bhavan Gandhi

*Corresponding author for this work

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

5 Scopus citations

Abstract

Content-based image retrieval has become an indispensable tool for managing the rapidly growing collections of digital images. The goal is to organize the contents semantically, according to meaningful categories. In recent papers we introduced a new approach for semantic image classification that relies on the adaptive perceptual color-texture segmentation algorithm proposed by Chen et al. This algorithm combines knowledge of human perception and signal characteristics to segment natural scenes into perceptually uniform regions. The resulting segments can be classified into semantic categories using region-wide features as medium level descriptors. Such descriptors are the key to bridging the gap between low-level image primitives and high-level image semantics. The segment classification is based on linear discriminant analysis techniques. In this paper, we examine the classification performance (precision and recall rates) when different sets of region-wide features are used. These include different color composition features, spatial texture, and segment location. We demonstrate the effectiveness of the proposed techniques on a database that includes 9000 segments from approximately 2500 photographs of natural scenes.

Original languageEnglish (US)
Title of host publication2006 IEEE International Conference on Image Processing, ICIP 2006 - Proceedings
Pages2921-2924
Number of pages4
DOIs
StatePublished - 2006
Event2006 IEEE International Conference on Image Processing, ICIP 2006 - Atlanta, GA, United States
Duration: Oct 8 2006Oct 11 2006

Publication series

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

Other

Other2006 IEEE International Conference on Image Processing, ICIP 2006
CountryUnited States
CityAtlanta, GA
Period10/8/0610/11/06

Keywords

  • Adaptive perceptual color texture segmentation
  • Content-based image retrieval
  • Feature extraction
  • Segment classification
  • Semantic analysis

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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