Improved Content based Texture Image Classification using Cascade RBF
Neha Sahu, Vivek Jain · 2013
Content-based image retrieval (CBIR) systems aim to return the most relevant images in a database, according to the user’s opinion for a given query. Due to the dynamic nature of feature content of image of image user query are frequently changed and result of retrieval image are suffered. For the improvement of the capacity of image query retrieval used image classification technique, image classification is well known technique of supervised learning. The improved method of image classification increases the working efficiency of image query retrieval. For the improvements of classification technique we used RBF neural network function for better prediction of feature used in image retrieval. Our proposed method optimized the feature selection process and finally sends data to multiclass classifier for classification of data. Here we used support vector machine for multi-class classification. As a classifier SVM suffering two problems (1) how to choose optimal feature sub set input and (2) how to set best kernel parameters. These problems influence the performance and accuracy of support vector machine. Now the pre-sampling of feature reduced the feature selection process of support vector machine for image classification. Keywords— CBIR, Classification, SVM, RBF _________________________________________________________________________________________