Visual information quantification for object recognition and retrieval

The rapid development of computer vision has led to an increasing amount of 3 D data,such as multiple views and point clouds,which are widely used in 3 D object recognition and retrieval.Intuitively,the quality of 3 D data is the most crucial factor that directly affects the performance of 3 D applications.However,how to evaluate the 3 D data quality,especially the multi-view data quality,is still an open question.To tackle this issue,we propose an entropy-based multi-view information quantification model(MV-Info model)to quantitatively evaluate the multi-view data information.Our proposed MV-Info model consists of hierarchical data module,feature generation module,and quantitative calculation module.Besides,it considers the information entropy theory for more reasonable quantification results.In our method,how much information we can observe from a group of views can be quantified,which can be used to support 3 D recognition and retrieval.We also designed a series of experiments to evaluate the effectiveness of the proposed model.The experimental results demonstrate the rationality and validity of the proposed model.