ArticleOptik2020
An efficient image descriptor for image classification and CBIR.
Article in Optik, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed.
- Enhanced content-based image retrieval via hybrid color, texture, and deep learning features.Scientific reports · 2026Article
- Learning optimal image representations through noise injection for fine-grained search.Scientific reports · 2025Article
- Deep image features sensing with multilevel fusion for complex convolution neural networks & cross domain benchmarks.PloS one · 2025Article
- Unsupervised Content Mining in CBIR: Harnessing Latent Diffusion for Complex Text-Based Query Interpretation.Journal of imaging · 2024Article
- An image caption model based on attention mechanism and deep reinforcement learning.Frontiers in neuroscience · 2023Article
- Color Image Retrieval Method Using Low Dimensional Salient Visual Feature Descriptors for IoT Applications.Computational intelligence and neuroscience · 2023Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.