A Comparative Study of the Classification Algorithms Based on Feature Selection

  • A. Sravani
  • D. N. D. Harini
  • D. Lalitha Bhaskari
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 249)

Abstract

Image classification is a large and growing research field with its applications in the areas of CBIR (Content Based Image Retrieval), image mining and automatic image annotation. In this digital era there is a huge voluminous multimedia data available and the challenge lies in retrieving and classifying the most similar images based upon an input query. Images can be classified according to their nature, content or domain and Feature extraction is the key process to classify the images accordingly. In this paper, an attempt is made to calculate all the possible features of an image based on color, texture, shape, and statistical. Based up on the features the images are further classified, studied and compared with four Classification algorithms namely Naïve Bayes, Instance Based Learning, J48 and Random forest Classification. Further the classification is applied on a prescribed set of features, so as to test the best feature set for the query image to be classified. An image database of 1150 images divided into 17 categories are considered for Classification and a brief comparative study is done.

Keywords

Classification Image Classification Feature Extraction Classification Algorithms Naïve Bayes Random Forest Classification J48 Instance based learning 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • A. Sravani
    • 1
  • D. N. D. Harini
    • 1
  • D. Lalitha Bhaskari
    • 1
  1. 1.Dept. of CS&SEAndhra UniversityVisakhapatnamIndia

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