Shashwati Mishra

Work place: Department of Computer Science and Applications, Utkal University, Vani Vihar, Bhubaneswar, Odisha, India



Research Interests: Data Structures and Algorithms, Data Mining, Pattern Recognition, Artificial Intelligence


Shashwati Mishra has completed her M.C.A from Ravenshaw University in 2009 and M. Tech from KIIT University in 2011. She has received Gold medal from Ravenshaw University and Vice Chancellor’s Silver Medal from KIIT University for securing highest marks in M.C.A. and M. Tech respectively. She is presently pursuing Ph. D. in Utkal University and working as a lecture in Ravenshaw University. Her areas of interest include Data mining, Image mining, Artificial intelligence, Pattern recognition etc. She has more than 5 years of teaching experience.

Author Articles
A Histogram-based Classification of Image Database Using Scale Invariant Features

By Shashwati Mishra Mrutyunjaya Panda

DOI:, Pub. Date: 8 Jun. 2017

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. 

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