Abstract
Image annotation, i.e. mapping words into images, is currently a major research problem in image retrieval. In particular, images are usually segmented into a number of regions, and then low-level image features are extracted from the segmented regions for annotation. As the extracted image features may contain some noisy features, which could de- grade the recognition performance when the number of keywords assigned to images is very large, image feature selection needs to be considered. In this paper, a Pixel Density filter (PDfilter) and Information Gain (IG) are used as the feature se- lection techniques. By using Corel as the dataset, 10, 50, 100, 150 and 190 keywords annotation are setup for compari- sons. The experimental result shows that PDfilter and IG can increase the precision of image annotation by colour or tex- ture features. However, they do not enhance the annotation performance by the combined colour and texture features.
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