文章基本信息

标题：Content Based Leaf Image Retrieval (CBLIR) Intended for e-commerce
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作者：B.Sathya Bama ; S.Mohana Valli ; M.Aysha mariam 等
期刊名称：International Journal of Information Technology Convergence and Services (IJITCS)
印刷版ISSN：2231-1939
电子版ISSN：2231-153X
出版年度：2011
卷号：1
期号：2
出版社：AIRCC
摘要：This paper proposes an efficient computer-aided Plant Image Retrieval method based on plant leaf images using Shape and Texture features intended for e-commerce particularly in medical industry, botanical gardening, house plant identification etc. Log-Gabor wavelet is applied to the input image for texture feature extraction. Scale Invariant Feature Transform (SIFT) is incorporated to extract the feature points of the leaf image. Scale Invariant Feature Transform transforms an image into a large collection of feature vectors, each of which is invariant to image translation, scaling, and rotation. Results on a database of 500 plant images belonging to 45 different types of plants with different orientations scales, and translations show that proposed method outperforms the recently developed methods by giving 97.9% of retrieval efficiency for 20, 50, 80 and 100 retrievals.
关键词：Plant Image Retrieval; Scale Invariant Feature Transform; Log Gabor filter; and Retrieval Efficiency.