A Rank based Ensemble Classifier for Image Classification using Color and Texture Features

This paper presents a color image classificationmethod using rank based ensemble classifier. In this paper, weuse color histogram in different color spaces and Gaborwavelet to extract color and texture features respectively.These features are classified by two classifiers: NearestNeighbor (NN) and Multi Layer Perceptron (MLP). In theproposed approach, each set of features are classified by eachclassifier to generate a rank list of length three. Therefore, wehave some rank list for different combination of feature setsand classifiers. The generated rank lists present an ordered listof class labels that the classifier believes the input image isrelated to those classes in order of priority. To combine theoutputs (rank list) of each classifier, simple and weightedmajority vote are used. Experiments show the proposed systemwith weighted majority vote achieves a recall and precision of86.2 % and 86.16 % respectively. Our proposed system hashigher efficiency in comparison of other system

کلیدواژه ها:

color histogram ، content-based image retrieval ، ensemble classifier ، gabor wavelet ، image classification ، majority vote