Cross-domain learning methods for high-level visual concept classification

Exploding amounts of multimedia data increasingly require automatic indexing and classification, e.g. training classifiers to produce high-level features, or semantic concepts, chosen to represent image content, like car, person, etc. When changing the applied domain (i.e. from news domain to consumer home videos), the classifiers trained in one domain often perform poorly in the other domain due to changes in feature distributions. Additionally, classifiers trained on the new domain alone may suffer from too few positive training samples. Appropriately adapting data/models from an old domain to help classify data in a new domain is an important issue. In this work, we develop a new cross-domain SVM (CDSVM) algorithm for adapting previously learned support vectors from one domain to help classification in another domain. Better precision is obtained with almost no additional computational cost. Also, we give a comprehensive summary and comparative study of the stateof-the-art SVM-base...

• Type: Conference
• Year: 2008
• Where: ICIP
• Authors: Wei Jiang, Eric Zavesky, Shih-Fu Chang, Alexander C. Loui
• Keywords: Benchmark Data Set, ICIP 2008, Image Processing, Intuitive Selection Criterion, Mean Average Precision, Old Domain, Training Classifiers