User-Driven Fuzzy Clustering: On the Road to Semantic Classification

The work leading to this paper is semantic image classification. The aim is to evaluate contributions of clustering mechanisms to organize low-level features into semantically meaningful groups whose interpretation may relate to some description task pertaining to the image content. Cluster assignment reveals underlying structures in the data sets without requiring prior information. The semantic component indicates that some domain knowledge about the classification problem is available and can be used as part of the training procedures. Besides, data structural analysis can be applied to determine proximity and overlapping between classes, which leads to misclassification problems. This information is used to guide the algorithms towards a desired partition of the feature space and establish links between visual primitives and classes. It derives into partially supervised learning modes. Experimental studies are addressed to evaluate how unsupervised and partially supervised fuzzy clustering boost semantic-based classification capabilities.