Hybrid Clustering Techniques for Multimedia Information Retrieval

Abstract

The rapid development of multimedia data, including pictures, music, and videos, has made MIR, or Multimedia Information Retrieval, a distinct area of ongoing interest. Retrieval methodologies often fail to organize and process vast and heterogeneous datasets containing unformatted information. This issue has recently been addressed by hybrid approaches that combine multiple clustering techniques, such as hierarchical, density-based, partition-based, and model-based clustering, among others. Multimedia retrieval and user convenience are enhanced as these methods improve the understanding of content and its categorization. This document summarizes the hybrid clustering approaches and their use in MIR systems. The paper discusses the application of feature-level fusion and metadata, as well as semantic-based clustering, which broadens the scope of scaling and strengthening retrieval systems. Focusing on the multimedia database challenge of high-dimensional feature spaces, hybrid approaches that integrate unsupervised methods, such as k-means and DBSCAN, with supervised or semi-supervised techniques overcome the barriers of noisy data, pattern concealment, and adaptive flexibility. Special attention is given to applying deep learning with clustering models for feature extraction, where CNNs and autoencoders serve as powerful hierarchical feature extractors for multimedia data. The paper focuses on recent developments regarding hybrid clustering frameworks that incorporate ontologies, graph-based models, and multimodal fusion approaches to enhance contextual understanding and semantic retrieval alignment. The evaluation of benchmark datasets confirms that hybrid clustering outperforms traditional single-method approaches, achieving higher Precision, Recall, and F1 Scores. The discussion addresses the obstacles of computational complexity, real-time processing, and scalability. The integration of explainable AI and privacy-preserving MIR through federated learning presents promising possibilities. At the same time, hybrid clustering techniques provide the backbone for efficient and intelligent multimedia information retrieval, enabling more context-aware and user-centric retrieval in diverse applications, such as digital libraries, surveillance, social media, and healthcare.