Abstract—Content Based Image Retrieval (CBIR) is
important in computer aided plant species recognition. Texture
and shape information have been the primitive image
descriptors in content based image retrieval systems. This
paper presents a novel framework for combining both the
features, texture and shape information, and achieve higher
retrieval efficiency. The study provides a methodology for
retrieving medicinal plants images from a database of medicinal
plant images based on shape and texture features. The shape
descriptors include Zernike moment, Fourier descriptor (FD),
Generic fourier Descriptor(GFD) and for texture descriptors
gabor filters are used. The similarity measures, euclidean
distance of each medicinal plant image from the database to
query image is used. The images are sorted based on similarity
of Euclidean distance. The retrieval experiments are carried on
different training and test medicinal plant images. The
effectiveness of different descriptors is confirmed by the
experimental results. We have investigated shape and texture
features for medicinal plant retrieval by successively combining
the different transforms. The retrieval efficiency is reported
through precision and recall rate. Experimental results by
combining Gabor and Zernike transform outperforms the all
other methods.
Index Terms—CBIR, Fourier descriptor, medicinal plant,
zernike moments.
Basavaraj S. Anam is with the K.L.E. Institute of Technology, HUBLI,
and Karnataka, India (e-mail:anami_basu@hotmail.com)
Suvarna S Nandyal is with the JNTU Hyderabad, AP, India and Dept of
CSE,P.D.A.College of engg, GULBARGA, Karnataka, India
(e-mail:suvarna_nandyal@yahoo.com)
A. Govardhan is with the JNTU Hyderabad, Andhra Pradesh, India
(e-mail:govardhan_cse@yahoo.co.in).
Cite:Basavaraj S. Anami, Suvarna S. Nandyal, and A. Govardhan, "Suitability of Shape and Texture Features in Retrieval of Medicinal Plants’ Images in Indian Context," International Journal of Machine Learning and Computing vol.2, no. 6, pp. 848-854, 2012.