A self-adaptive automatic albuming system

Classification of digital photos using low-level features is an important but very difficult issue in many applications that deal with consumer photographs. Previous methods for image classification problems always aimed at some specific classification problems such as city vs. landscape. These methods were always based on some specific ground truths and they were not suitable for general classification problems. A system based on Bayesian framework and learning mechanism was proposed. The most effective and efficient classification strategy could be automatically obtained. The system was implemented and performance test was conducted using a database of real consumer photos. Experimental results show that high accuracy can be obtained for general classification problems.