Semantic image classification based on Bayesian framework and one-step relevance feedback
Guanghuan Hu, Jiajun Bu, Chun Chen · 2004
Grouping photos into semantically meaningful categories is an important issue in many applications that use low-level features to deal with consumer photographs. However, low-level features such as color and texture did not contain the local and spatial properties of images. And high accuracy cannot be obtained for general semantic classification problems. An approach based on Bayesian framework and one-step relevance feedback was proposed. Knowledge from low-level features and spatial properties was integrated into Bayesian framework. Furthermore, a one-step relevance feedback method was implemented to specify the optimal division strategy of images. The system provides the ability to utilize the local and spatial properties to classify new images. Experimental results show that high accuracy can be obtained for general semantic classification problems.