Automatic Image Annotation Using Adaptive Color Classification

We describe a system which automatically annotates images with a set of prespecified keywords, based on supervised color classification of pixels intoNprespecified classes using simple pixelwise operations. The conditional distribution of the chrominance components of pixels belonging to each class is modeled by a two-dimensional Gaussian function, where the mean vector and the covariance matrix for each class are estimated from appropriate training sets. Then, a succession of binary hypothesis tests with image-adaptive thresholds has been employed to decide whether each pixel in a given image belongs to one of the predetermined classes. To this effect, a universal decision threshold is first selected for each class based on receiver operating characteristics (ROC) curves quantifying the optimum “true positive” vs “false positive” performance on the training set. Then, a new method is introduced for adapting these thresholds to the characteristics of individual input images based on histogram cluster analysis. If a particular pixel is found to belong to more than one class, a maximuma posterioriprobability (MAP) rule is employed to resolve the ambiguity. The performance improvement obtained by the proposed adaptive hypothesis testing approach over using universal decision thresholds is demonstrated by annotating a database of 31 images.