IEEE Conference on Computer Vision and Pattern Recognition, Minnesota, Amerika Birleşik Devletleri, 17 - 22 Haziran 2007, ss.3620-3621
Automatic image annotation systems available in the literature concatenate color, texture and/or shape features in a single feature vector to learn a set of high level semantic categories using a single learning machine. This approach is quite naive to map the visual features to high level semantic information concerning the categories. Concatenation of many features with different visual properties and wide dynamical ranges may result in curse of dimensionality and redundancy problems. Additionally, it usually requires normalization which may cause an undesirable distortion in the feature space.