Volume & Issue no: Volume 3, Issue 7, July 2014
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Title: |
Column Transform based Feature Generation for Classification of Image Database |
Author Name: |
H.B.Kekre, Tanuja K Sarode and Jagruti K Save |
Abstract: |
ABSTRACT
Designing computer programs to automatically classify images using low level or high level features is a challenging task in
image processing. This paper proposes an efficient classification technique which is based on image transforms and nearest
neighbor classification. The database of images which has been used for experimentation is large containing 2000 images (20
classes) with wide variety in them. Since the performance of classifier is largely depends on the feature vector, a lot of research is
going on the feature generation methods. This paper analyses the different transforms in this application domain. Initially
Transforms like Discrete Fourier Transform(DFT), Discrete Cosine Transform(DCT), Discrete Sine Transform(DST), Hartley
Transform , Walsh Transform and Kekre Transform applied to the columns of three planes of color image. Then using fusion
technique, feature vector is generated. For more dimension reduction, the size of vector is further reduced. Nearest neighbor
classification with Euclidean distance as similarity measure is used for classification task. The performance of other similarity
measures like Manhattan distance, Cosine correlation measure and Bray-Curtis distance are also tested. The paper also discusses
the accuracy obtained for variation in the size of feature vector, the size of training set and its impact on the result.
Keywords: Image classification, Image Transform, Nearest neighbor Classifier, Similarity Measure, Feature vector
generation, Row mean vector |
Cite this article: |
H.B.Kekre, Tanuja K Sarode and Jagruti K Save , "
Column Transform based Feature Generation for Classification of Image Database" , International Journal of Application or Innovation in Engineering & Management (IJAIEM) ,
Volume 3, Issue 7, July 2014 , pp.
172-181 , ISSN 2319 - 4847.
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