A novel Bayesian framework for indoor-outdoor image classification
Guanghuan Hu, Jiajun Bu, Chun Chen · 2004
An approach based on Bayesian framework and relevance feedback is proposed to improve the accuracy of indoor-outdoor image classification. In the system, knowledge from low-level features and spatial properties are integrated in Bayesian framework, and a relevance feedback method is implemented to specify the optimal weights of sub-blocks of images. The system provides the ability to utilize the local and spatial properties to classify new images. Performance testing of the algorithm is conducted using a database of real consumer photos. Experimental results over more than 1500 images show that high accuracy could be obtained using the spatial properties.