A study of block-global feature based supervised image annotation

In order to get better semantic annotation performance, block-global features are extracted as low-level visual features for image semantic annotation. Specifically, wellknown global feature extraction method, namely two-dimensional principal component analysis (2DPCA) is applied to extract the image block-global features. Unlike typical image annotation methods which use local features or global features separately, we propose to extract global features from image local regions (block) with the expectation of: a) combining the advantages of local and global features; b) discovering multiple semantic meanings in one image. In the experiment, comparative studies have been done for the performance of block-global feature extraction methods with widely used local feature extraction method such as scale invariant feature transform. The results show that 2DPCA has a significantly better performance than the performance of other methods.