Automatic image annotation and retrieval using cross-media relevance models

Libraries have traditionally used manual image annotation for indexing and then later retrieving their image collections. However, manual image annotation is an expensive and labor intensive procedure and hence there has been great interest in coming up with automatic ways to retrieve images based on content. Here, we propose an automatic approach to annotating and retrieving images based on a training set of images. We assume that regions in an image can be described using a small vocabulary of blobs. Blobs are generated from image features using clustering. Given a training set of images with annotations, we show that probabilistic models allow us to predict the probability of generating a word given the blobs in an image. This may be used to automatically annotate and retrieve images given a word as a query. We show that relevance models. allow us to derive these probabilities in a natural way. Experiments show that the annotation performance of this cross-media relevance model is ...

• Type: Conference
• Year: 2003
• Where: SIGIR
• Authors: Jiwoon Jeon, Victor Lavrenko, R. Manmatha
• Keywords: Image, Image Annotation, Manual Image Annotation, SIGIR 2003