Deep Hybrid Model for Semantic Image Retrieval | #sciencefather #scientistaward #database #ResearchInnovation #AcademicAI #AIResearch #PhDResearch #ScientificComputing

A Multi-Modal Hybrid Deep Learning Model for Efficient Content-Based Image Retrieval

Motivation and Challenges in Image Retrieval

With the explosive growth of visual data in domains such as healthcare, surveillance, digital archiving, and e-commerce, there is an increasing demand for intelligent systems capable of accurately retrieving relevant images based on their content. Traditional keyword- or filename-based retrieval methods are inefficient and often irrelevant, as they fail to capture the actual visual content of images. Moreover, the “semantic gap” — the difference between low-level pixel data and high-level human interpretation — continues to be a major bottleneck in effective image search systems. There is also a rising challenge of handling high-dimensional data, redundant features, and variations in lighting, texture, or viewpoint, which impacts retrieval precision and computational speed.


Hybrid Feature Extraction Approach

To overcome these limitations, the proposed system employs a hybrid feature extraction mechanism by combining three categories of features:

  • Contextual Features: These are extracted using LBP and HOG techniques applied to image segments, helping to capture local texture and edge information.

  • Basic Features: These include statistical metrics and GLCM-based features, offering global texture and intensity distribution insights.

  • Deep Features: Obtained from a CNN model, these provide high-level semantic understanding of image content.

The combination of these features enables a more comprehensive and discriminative representation of the image, addressing both low-level and high-level characteristics.

Feature Selection via Reinforcement Learning

One of the major contributions of this system is the integration of reinforcement learning-based feature selection. After concatenating the extracted feature vectors, a learning automata-based agent is employed to identify the most relevant features. By evaluating clustering performance as a feedback reward, the system learns to discard redundant or noisy features and retain only the most discriminative ones. This reduces the dimensionality of the feature space and improves retrieval speed and precision.

Clustering and Retrieval Mechanism

Once the optimal feature subset is selected, the images are grouped using a Fuzzy C-Means (FCM) clustering algorithm. During the retrieval phase, the system compares the query image with cluster centroids and retrieves the closest matching images. This soft clustering approach allows for overlapping clusters and more flexible retrieval compared to hard clustering techniques.

Experimental Evaluation

The proposed method is evaluated on benchmark datasets including Corel-1000 and ALOI, known for their diversity in color, texture, and object orientation. Results show that the hybrid feature model, combined with reinforcement learning for feature selection, significantly outperforms traditional CBIR methods in terms of accuracy, precision, and retrieval speed.

Applications and Future Work

This hybrid model holds promise for a range of real-world applications such as medical image diagnosis, security surveillance, art and cultural heritage preservation, and e-commerce visual search. Future directions include extending the system to support multi-label image retrieval, incorporating transformer-based models, and testing performance on real-time and large-scale databases.

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