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Related Experiment Video

Updated: Dec 22, 2025

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

An efficient image descriptor for image classification and CBIR.

Ashkan Shakarami¹, Hadis Tarrah²

¹Department of Computer Engineering, Afarinesh Institute of Higher Education, Boroujerd, Iran.

|May 7, 2020

Summary

This summary is machine-generated.

This study introduces an efficient image description method using improved AlexNet, Histogram of Oriented Gradients (HOG), and Local Binary Patterns (LBP) for better image classification and retrieval. The approach enhances accuracy and mean Average Precision (mAP) while reducing computational complexity.

Keywords:

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

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Area of Science:

Computer Vision
Machine Learning
Deep Learning

Background:

Image classification and Content-Based Image Retrieval (CBIR) performance heavily rely on effective pattern recognition and feature extraction.
Machine Learning (ML) and Deep Learning (DL) algorithms are increasingly adopted to address these challenges.

Purpose of the Study:

To propose an efficient and accurate image description method for enhanced image classification and CBIR.
To leverage a combination of advanced ML/DL techniques for superior feature extraction.

Main Methods:

Developed a novel method combining an improved AlexNet Convolutional Neural Network (CNN) with Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP) descriptors.
Employed Principle Component Analysis (PCA) for effective dimension reduction of extracted features.

Main Results:

The proposed method demonstrated superior performance over existing techniques on Corel-1000, OT, and FP datasets.
Achieved significant improvements in accuracy and mean Average Precision (mAP).
Successfully reduced computational complexity, indicating efficiency.

Conclusions:

The integrated approach of improved AlexNet, HOG, LBP, and PCA offers a powerful solution for image description.
This method provides a robust and efficient framework for image classification and CBIR tasks.