A Semantic-Similarity-Based Method for Object Description and Clustering

Object recognition and clustering are useful techniques in pattern recognition and computer vision. Traditionally, these techniques have been implemented by visual-feature-based methods. However, these methods may not adequately tackle the differences in the shapes and colors of objects. In this paper, we propose an alternative method in which objects of different colors, or even different shapes, function similarly. If text strings are visible on their surfaces, we can extract the semantic features of objects, thereby recognizing and clustering them. Thus, this method is based on semantic information. The method is experimentally tested on a dataset of images containing the packing cases of commercial products. Semantic information in the dataset images is retrieved using text extraction modules, passed through an Internet data mining module and is finally described and clustered. The final clustering results are more accurate than those obtained by visual-feature-based methods.