Urticaceae Juss.is a large cosmopolitan family and taxonomically difficult group,partly because it encompasses a broad range of morphological diversity and many of the diagnostic characters（e.g.flower,achene.stipule,...Urticaceae Juss.is a large cosmopolitan family and taxonomically difficult group,partly because it encompasses a broad range of morphological diversity and many of the diagnostic characters（e.g.flower,achene.stipule,bract）require a microscope for accurate determination.Meanwhile,most Uriiceae species have stinging hairs which make them more difficult to collect and identify.As a result,the infra-familial classification of Urticaceae has been controversial for more than a century.A research group led by Prof.展开更多
Flower image retrieval is a very important step for computer-aided plant species recognition. In this paper, we propose an efficient segmentation method based on color clustering and domain knowledge to extract flower...Flower image retrieval is a very important step for computer-aided plant species recognition. In this paper, we propose an efficient segmentation method based on color clustering and domain knowledge to extract flower regions from flower images. For flower retrieval, we use the color histogram of a flower region to characterize the color features of flower and two shape-based features sets, Centroid-Contour Distance (CCD) and Angle Code Histogram (ACH), to characterize the shape features of a flower contour. Experimental results showed that our flower region extraction method based on color clustering and domain knowledge can produce accurate flower regions. Flower retrieval results on a database of 885 flower images collected from 14 plant species showed that our Region-of-Interest (ROI) based retrieval approach using both color and shape features can perform better than a method based on the global color histogram proposed by Swain and Ballard (1991) and a method based on domain knowledge-driven segmentation and color names proposed by Das et al.(1999).展开更多
文摘Urticaceae Juss.is a large cosmopolitan family and taxonomically difficult group,partly because it encompasses a broad range of morphological diversity and many of the diagnostic characters（e.g.flower,achene.stipule,bract）require a microscope for accurate determination.Meanwhile,most Uriiceae species have stinging hairs which make them more difficult to collect and identify.As a result,the infra-familial classification of Urticaceae has been controversial for more than a century.A research group led by Prof.
基金Project (Nos. 60302012 60202002) supported by the NationaNatural Science Foundation of China and the Research GrantCouncil of the Hong Kong Special Administrative Region (NoPolyU 5119.01E) China
文摘Flower image retrieval is a very important step for computer-aided plant species recognition. In this paper, we propose an efficient segmentation method based on color clustering and domain knowledge to extract flower regions from flower images. For flower retrieval, we use the color histogram of a flower region to characterize the color features of flower and two shape-based features sets, Centroid-Contour Distance (CCD) and Angle Code Histogram (ACH), to characterize the shape features of a flower contour. Experimental results showed that our flower region extraction method based on color clustering and domain knowledge can produce accurate flower regions. Flower retrieval results on a database of 885 flower images collected from 14 plant species showed that our Region-of-Interest (ROI) based retrieval approach using both color and shape features can perform better than a method based on the global color histogram proposed by Swain and Ballard (1991) and a method based on domain knowledge-driven segmentation and color names proposed by Das et al.(1999).
