Multistrategical image classification for image data mining

• Authors: Hiroki Nomiya;Kuniaki Uehara
• Venue: Proceedings of the 8th international workshop on Multimedia data mining: (associated with the ACM SIGKDD 2007)
• Year: 2007

For an efficient image data mining, accurately finding and retrieving various types of images are required. Therefore, there is a need for an image classification method which can be widely applicable to image data mining tasks. But traditional methods can be applied only limited domains. In this paper, we propose a flexible and accurate image classification method. Our method adopts a visual learning framework, which is an effective image classification framework based on machine learning. Currently, most of visual learning methods adopt monostrategy learning frameworks using a single learning algorithm. However, the real-world objects are too complex to be correctly recognized by a monostrategy method. Thus, utilizing a wide variety of features is essential to precisely discriminate them. In order to utilize various features, we propose multistrategical visual learning by integrating multiple visual learners. In our method, a visual learner is trained using the examples misclassified by the other visual learners. Therefore, all the visual learners can be collaboratively trained. This complementary learning framework leads to a more efficient classification.