Survey on Image clustering : Techniques, Challenges, and Future Perspectives
Junqi Zhang, Feng Li, Bingbing Wang · Deep Learning and Pattern Recognition · 2024
Visual Data Analysis: Exploring the Significance, Hurdles, and Evolutionary Trajectory of Image clustering Techniques. In the realm of visual data analysis, Image clustering emerges as a potent instrument with the capacity to unveil the underlying architecture and motifs within datasets. Its utility spans across diverse domains, including social network examination, bioinformatics, and recommendation systems. This manuscript delves into the fundamentals and methodologies of Image clustering , encompassing classic spectral clustering, modularity maximization, and the cutting-edge application of deep learning in Imageneural network clustering. The document also scrutinizes the obstacles encountered in the practical application of Image clustering , such as the surge in data volume and intricacy, computational efficiency limitations, and the consistency of clustering outcomes. Looking ahead, the paper forecasts the evolutionary path of Image clustering technology, which includes algorithmic innovation, interdisciplinary amalgamation, and the broadening of its application scope, thereby offering robust solutions to intricate data analysis challenges. This comprehensive review serves as a valuable resource and catalyst for researchers and practitioners in the Image clustering domain, fostering the ongoing advancement and broader adoption of these techniques.