Image Annotation with Multiple Quantization

Image annotation plays an important role in image retrieval and understanding. Various techniques have been proposed for assigning keywords to images. One of the most frequently used methods is to search annotated images with similar visual features, and keywords are transfered to new coming images. This leads to the problem of nearest neighbor search, which is a hot topic of pattern recognition, information retrieval, and data compression. In this paper we proposed a fast and effective method for retrieving similar images from large collections of annotated images. The proposed technique employs discrete cosine transform and regular lattice quantization to encode images and search similar images directly with the corresponding codes. This technique is evaluated on image annotation. Similar images are retrieved by utilizing our encoding strategy, and keywords are assigned by utilizing traditional label transfer mechanism. Experimental results show that our method provides competitive performance with traditional methods, and mean while provides one scalable framework for annotating large collections of image dataset.