A New Method for Classification of Images Using Convolutional Neural Network Based on Dwt-Svd Perceptual Hash Function

Abstract

This paper proposes a method by using Convolutional Neural Network (CNN), which reduces the image classification time and maintains the classification performance above an acceptable threshold. A hybrid model called Discrete Wavelet Transform- Singular Value Decomposition based Perceptual Hash Convolutional Neural Network (DWT-SVD-PH-CNN) is proposed by using a perceptual hash function together with CNN to reduce the classification time. In the proposed method, the DWT-SVDbased perceptual hash function is used. The most important feature of perceptual hash functions is to obtain the salient features of images. First, DWT-SVD based perceptual hash function is applied to images for obtaining salient features. Then, images making up of salient features, are produced in 32x32 format and given as inputs to CNN, where Support Vector Machine (SVM) is used to classify the images. In this paper, the DWT-SVD-PH-CNN method is applied to Caltech 101 image database. Experimental results show that the proposed DWT-SVD-PH-CNN method has a high accuracy, about 95.8 %. Moreover, this method reduces the execution time from 241.21 seconds to 83.08 seconds compared to the classical method. Thus, the experimental results show that the proposed DWT-SVD-PH-CNN method performs much faster than classical CNN by maintaining the image classification accuracy high.