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Plant Image Retrieval Using Color, Shape, and Texture Features
Authors: H. Kebapci, B. Yanikoglu, G. Unal
Published in: The Computer Journal, 2010
Publication year: 2011
Abstract: We present a content-based image retrieval system for plant image retrieval, intended especially for 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 wellknown 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.
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