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Classification, bag of features, decision tree, histogram, feature extraction
Development of advanced technology has increased the size of data and has also created different categories of data. Classifying these different categories of data is the need of the era. We have proposed a method of classifying the image database containing four categories of images like human face, airplane, cup and butterfly. Our approach involves steps like feature extraction, bag of feature creation, histogram representation and classification using decision tree. For feature extraction SIFT (Scale Invariant Feature Transform) algorithm is used since it is invariant to rotation, change of scale, illumination etc. After extracting the features the bag of features concept is used to group the features using k-means clustering algorithm. Then a histogram is plotted for each image in the image database which represents the distributions of data in different clusters. In the final step the most robust, simple and flexible decision tree algorithm is applied on the table created from the histogram plots to obtain the classification result. The experimental observations and the calculated accuracy proves that this method of classification works well for classifying an image dataset having different categories of images.
Shashwati Mishra, Mrutyunjaya Panda,"A Histogram-based Classification of Image Database Using Scale Invariant Features", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.9, No.6, pp.55-64, 2017. DOI: 10.5815/ijigsp.2017.06.07
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