An Overview of Content-Based Image Retrieval Methods and Techniques

Hadid, M.H; Hussein, Qasim Mohammed; Al-Qaysi, Z.T.; Ahmed, M.A.; Salih, Mahmood M.

doi:10.52866/ijcsm.2023.02.03.006

Cited by 7 publications

References 80 publications

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Enhanced Content-Based Image Retrieval through Integrated Local Average Binary Patterns and Joint Color Probabilities

Hosseini,

Eshghi,

Mohammadi

et al. 2024

Preprint

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Artificial intelligence techniques for image pattern recognition and retrieval are pivotal in various applications, particularly in medical imaging. Despite numerous existing methods, the process remains complex and computationally intensive. This study introduces an innovative approach to content-based image retrieval (CBIR) by integrating Local Average Binary Patterns (LABP) and the joint probability distribution of color channels. LABP extends the traditional Local Binary Pattern (LBP) by considering multiple layers of neighboring pixels, enabling a more comprehensive texture representation. Additionally, we propose a novel color feature extraction method based on the discrete joint probability distribution of RGB color channels, providing a robust representation of color information. The effectiveness of the proposed method is validated on the Wang (Corel-1k) and Corel-10k datasets, demonstrating superior precision compared to other state-of-the-art techniques. This work contributes to enhancing CBIR performance by combining these novel features into a unified feature vector, improving efficiency and accuracy, especially in large datasets. The code and links to datasets are publicly available at https://github.com/BU-AILab/LABP.

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Enhanced Content-Based Image Retrieval through Integrated Local Average Binary Patterns and Joint Color Probabilities

Hosseini,

Eshghi,

Mohammadi

et al. 2024

Preprint

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Secureimagesec: A privacy-preserving framework for outsourced picture representation with content-based image retrieval

Jayashree

2024

IDA

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Content-Based Image Retrieval (CBIR) uses complicated algorithms to analyze visual attributes and retrieve relevant photos from large databases. CBIR is essential to a privacy-preserving feature extraction and protection method for outsourced picture representation. SecureImageSec combines essential methods with the system’s key entities to ensure secure, private and protected image feature processing during outsourcing. For a system to be implemented effectively, these techniques must be seamlessly integrated across critical entities, such as the client, the cloud server that is being outsourced, the component that protects secure features, the component that maintains privacy in communication, access control, and authorization, and the integration and system evaluation. The client entity initiates outsourcing using advanced encryption techniques to protect privacy. SecureImageSec protects outsourced data by using cutting-edge technologies like Fully Homomorphic Encryption (FHE) and Secure Multi-Party Computation (SMPC). Cloud servers hold secure feature protection entities and protect outsourced features’ privacy and security. SecureImageSec uses AES and FPE to protect data format. SecureImageSec’s cloud-outsourced privacy-preserving communication uses SSL/TLS and QKD to protect data transmission. Attribute-Based Encryption (ABE) and Functional Encryption (FE) in SecureImageSec limit access to outsourced features based on user attributes and allow fine-grained access control over decrypted data. SecureImageSec’s Information Leakage Rate (ILR) of 0.02 for a 1000-feature dataset shows its efficacy. SecureImageSec also achieves 4.5 bits of entropy, ensuring the encrypted feature set’s muscular cryptographic strength and randomness. Finally, SecureImageSec provides secure and private feature extraction and protection, including CBIR capabilities, for picture representation outsourcing.

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A novel deep learning based CBIR model using Convolutional Siamese Neural Networks

John,

Mathew,

Bindu

2024

IFS

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Content-Based Image Retrieval (CBIR) is a technique that involves retrieving similar images from a large database by analysing the content features of the query image. The heavy usage of digital platforms and devices has in a way promoted CBIR and its allied technologies in computer vision and artificial intelligence. The process entails comparing the representative features of the query image with those of the images in the dataset to rank them for retrieval. Past research was centered around handcrafted feature descriptors based on traditional visual features. But with the advent of deep learning the traditional manual method of feature engineering gave way to automatic feature extraction. In this study, a cascaded network is utilised for CBIR. In the first stage, the model employs multi-modal features from variational autoencoders and super-pixelated image characteristics to narrow down the search space. In the subsequent stage, an end-to-end deep learning network known as a Convolutional Siamese Neural Network (CSNN) is used. The concept of pseudo-labeling is incorporated to categorise images according to their affinity and similarity with the query image. Using this pseudo-supervised learning approach, this network evaluates the similarity between a query image and available image samples. The Siamese network assigns a similarity score to each target image, and those that surpass a predefined threshold are ranked and retrieved. The suggested CBIR system undergoes testing on a widely recognized public dataset: the Oxford dataset and its performance is measured against cutting-edge image retrieval methods. The findings reveal substantial enhancements in retrieval performance in terms of several standard benchmarks such as average precision, average error rate, average false positive rate etc., providing strong support for utilising images from interconnected devices.

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An Overview of Content-Based Image Retrieval Methods and Techniques

Cited by 7 publications

References 80 publications

Enhanced Content-Based Image Retrieval through Integrated Local Average Binary Patterns and Joint Color Probabilities

Enhanced Content-Based Image Retrieval through Integrated Local Average Binary Patterns and Joint Color Probabilities

Secureimagesec: A privacy-preserving framework for outsourced picture representation with content-based image retrieval

A novel deep learning based CBIR model using Convolutional Siamese Neural Networks

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