Experiences with shape classification through fuzzy c-means using geometrical and moments descriptors

Abstract

Due to the growing diffusion of digital media, most of real world applications have data with multiple modalities, from multiple sources and in multiple formats. The modelling of information coming from multimedia sources represents an important issue for applications which achieve multimedia mining activities. In particular, the last decades have witnessed great interest in image processing by "mining" visual information for objects recognition and retrieval. Some studies have revealed the image disambiguation based on the shape produces better results than features such as color or texture; moreover, the classification of objects extracted from an image database appears more intuitively formulated as a shape classification task. This paper presents an approach for 2D shapes classification. The approach is based on the combined use of geometrical and moments features extracted by a given collection of images and achieves shape-based classification exploiting fuzzy clustering techniques. © 2012 Springer-Verlag.