Two-Probabilistic Latent Semantic Model for Image Annotation and Retrieval

A novel latent variable modeling technique for image annotation and retrieval is proposed. This model is useful for annotating the images with relevant semantic meanings as well as for retrieving images which satisfy the users query with specific text or image. The framework of two-step latent variable is proposed to support multi-functionality of the retrieval and annotation system. Furthermore, the existing and the proposed image annotation models are compared in terms of their annotating performance. Images from standard databases are used in the comparison in order to identify the best model for automatic image annotation, using precision-recall measurement. Local features, or visual words, of each image in the database are extracted using Scale-Invariant Feature Transform (SIFT) and clustering techniques. Each image is then represented by Bag-of-Features (BoF) which is a histogram of visual words. Semantic meanings can then be related to each BoF using latent variable for annotation purposes. Subsequently, for image retrieval, each image query is also related to semantic meanings. Finally, image retrieval results are obtained by matching semantic meanings of the query with those of the images in the database using a second latent variable.