An efficient image descriptor for image classification and CBIR

Abstract

Pattern recognition and feature extraction of images always have been important subjects in improving the performance of image classification and Content-Based Image Retrieval (CBIR). Recently, Machine Learning and Deep Learning algorithms are utilized widely in order to achieve these targets. In this research, an efficient method for image description is proposed which is developed by Machine Learning and Deep Learning algorithms. This method is created using combination of an improved AlexNet Convolutional Neural Network (CNN), Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP) descriptors. Furthermore, the Principle Component Analysis (PCA) algorithm has been used for dimension reduction. The experimental results demonstrate the superiority of the offered method compared to existing methods by improving the accuracy, mean Average Precision (mAP) and decreasing the complex computation. The experiments have been run on Corel-1000, OT and FP datasets.

Keywords: Content-based image retrieval (CBIR); Convolutional neural network (CNN); Image classification; Image descriptor; Machine learning; Pattern recognition.

Fig. 1

Proposed method.

Plot 1

The mAP-Mean recall plot of the proposed method for Corel-1000 dataset.

Plot 2

The mAP-Mean recall plot of the proposed method for OT dataset.

Plot 3

The mAP-Mean recall plot of the proposed method for FP dataset.