Plant image retrieval using color, shape and texture features

Hanife Kebapci, Berrin Yanikoglu*, Gozde Unal

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

74 Citations (Scopus)

Abstract

We present a content-based image retrieval system for plant image retrieval, intended especially for the house plant identification problem. A plant image consists of a collection of overlapping leaves and possibly flowers, which makes the problem challenging. We studied the suitability of various well-known color, shape and texture features for this problem, as well as introducing some new texture matching techniques and shape features. Feature extraction is applied after segmenting the plant region from the background using the max-flow min-cut technique. Results on a database of 380 plant images belonging to 78 different types of plants show promise of the proposed new techniques and the overall system: in 55% of the queries, the correct plant image is retrieved among the top-15 results. Furthermore, the accuracy goes up to 73% when a 132-image subset of well-segmented plant images are considered.

Original languageEnglish
Pages (from-to)1475-1490
Number of pages16
JournalComputer Journal
Volume54
Issue number9
DOIs
Publication statusPublished - Sept 2011
Externally publishedYes

Keywords

  • Gabor wavelets
  • image retrieval
  • plants
  • SIFT

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