Automatic Image Annotation Using Multi-object Identification

Due to the prevalence of digital cameras, it is easy to retrieve digital images from the Internet. With the rapid development of digital image processing, databases, and Internet technologies, how to efficiently manage a large amount of digital images is very important. In this paper, we proposed a novel approach for automatic image annotation. We extract color, texture, and shape features from a set of training images to build the main object classifier and background object models by using Support Vector Machine (SVM). We apply JSEG to segment background objects out of images, and then extract the feature vectors from the segmented objects for identification. In order to prevent over-segmenting the main object, the combination of Active Contour Model and JSEG is proposed to improve the system performance. Since the images in the same class have background consistency, we exploit Gaussian mixture model (GMM) to explore the relationship between image classes and image backgrounds, and build the association knowledge base. After classifying test images, we only need to compare the backgrounds with the related models for classification. Finally, the experimental results show that the proposed method has high effectiveness for image annotation.