title: A flower image retrieval method based on ROI feature.
abstract: 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).
authors.affiliation: Department of Applied Mathematics, Zhejiang University, Hangzhou 310027, China. Hax@nbit.gov.cn
authors.forename: An-Xiang
authors.initials: AX
authors.lastname: Hong
authors.forename: Gang
authors.initials: G
authors.lastname: Chen
authors.forename: Jun-Li
authors.initials: JL
authors.lastname: Li
authors.forename: Zhe-Ru
authors.initials: ZR
authors.lastname: Chi
authors.forename: Dan
authors.initials: D
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003116:Color; D003124:Colorimetry / Q000379:methods*; D035264:Flowers / Q000033:anatomy & histology* / Q000145:classification*; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated / Q000379:methods*; D010781:Photography / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted