Texture Features and KNN in Classification of Flower Images

In this paper, we propose an algorithmic model for automatic classification of flowers using KNN classifier. The proposed algorithmic model is based on textural features such as Gray level co-occurrence matrix and Gabor responses. A flower image is segmented using a threshold based method. The data set has different flower species with similar appearance (small inter class variations) across different classes and varying appearance (large intra class variations) within a class. Also, the images of flowers are of different pose with cluttered background under varying lighting conditions and climatic conditions. The flower images were collected from World Wide Web in addition to the photographs taken up in a natural scene. Experimental Results are presented on a dataset of 1250 images consisting of 25 flower species. It is shown that relatively a good performance can be achieved, using KNN classifier algorithm. A qualitative comparative analysis of the proposed method with other well known existing flower classification methods is also presented. General Terms Pattern Recognition, Image Processing, Algorithms

[1]  Marie-Pierre Jolly,et al.  Interactive Graph Cuts for Optimal Boundary and Region Segmentation of Objects in N-D Images , 2001, ICCV.

[2]  S. Ninomiya,et al.  Quantitative evaluation of flower colour pattern by image analysis and principal component analysis of Primula sieboldii E. Morren , 2004, Euphytica.

[3]  Chandrika Kamath,et al.  Retrieval using texture features in high-resolution multispectral satellite imagery , 2004, SPIE Defense + Commercial Sensing.

[4]  Marie-Pierre Jolly,et al.  Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[5]  Edward M. Riseman,et al.  Indexing Flower Patent Images Using Domain Knowledge , 1999, IEEE Intell. Syst..

[6]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[7]  William A. Barrett,et al.  Intelligent scissors for image composition , 1995, SIGGRAPH.

[8]  Takeshi Saitoh,et al.  Automatic recognition of wild flowers , 2003, Systems and Computers in Japan.

[9]  Andrew Zisserman,et al.  Delving into the Whorl of Flower Segmentation , 2007, BMVC.

[10]  Rafael C. González,et al.  Digital image processing using MATLAB , 2006 .

[11]  Andrew Zisserman,et al.  A Visual Vocabulary for Flower Classification , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[12]  Manik Varma,et al.  Learning The Discriminative Power-Invariance Trade-Off , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[13]  Takeshi Saitoh,et al.  Automatic recognition of blooming flowers , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[14]  Andrew Zisserman,et al.  Automated Flower Classification over a Large Number of Classes , 2008, 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing.