The Generic Object Classification Based on MIML Machine Learning
Lihua Guo, Lianwen Jin · 2009
Multi-instance and multi-label (MIML) machine learning has been employed in the generic object classification for its graceful performance in solving the ambiguity of image. The whole image is regarded as a multi-instance bag. The image is separated into four parts, whose edge's histograms are calculated. These input vectors can be combined a multi-instance ones for adapting the MIML learning. The experimental results show that the average precise ratio of our method is higher 3% than one of the traditional support vector machine method.