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

This research presents an advanced content-based image retrieval (CBIR) system that integrates contextual, statistical, and deep features to enhance retrieval accuracy and efficiency. The proposed method extracts Local Binary Patterns (LBP), Histogram of Oriented Gradients (HOG), Gray Level Co-occurrence Matrix (GLCM) features, and deep semantic features via CNN. These features are fused into a single vector, and a reinforcement learning-based feature selection algorithm is applied to reduce dimensionality, eliminate redundancy, and improve precision. The refined feature set is then used in a Fuzzy C-Means clustering model for effective image matching and retrieval. Tested on Corel-1000 and ALOI datasets, this multi-stage hybrid framework addresses the semantic gap and computational challenges in large-scale image databases, offering a robust solution for diverse applications like medical imaging, digital archiving, and visual recognition.

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