Trong-Ton Pham

Latent semantic fusion model for image retrieval and annotation

This paper studies the effect of Latent Semantic Analysis (LSA) on two different tasks: multimedia document retrieval (MDR) and automatic image annotation (AIA). The contributions of this paper are twofold. First, to the best of our knowledge, this work is the first study of the influence of LSA on the retrieval of a significant number of multimedia documents (i.e. collection of 20000 tourist images). Second, it shows how different image representations (region-based and keypoint-based) can be combined by LSA to improve automatic image annotation. The document collections used for these experiments are the Corel photo collection and ImageCLEF 2006 collection.

Spatial relationships in visual graph modeling for image categorization

In this paper, a language model adapted to graph-based representation of image content is proposed and assessed. The full indexing and retrieval processes are evaluated on two different image corpora. We show that using the spatial relationships with graph model has a positive impact on the results of standard Language Model (LM) and outperforms the baseline built upon the current state-of-the-art Support Vector Machine (SVM) classification method.

Integration of spatial relationships in visual language model for scene retrieval

In this paper, we describe a method to use a graph-based language modeling approach for image retrieval and image categorization. We first mapped image regions to induced concepts and then spatial relationships between these regions to build a graph representation of images. Our method allows to deal with different scenarii, where isolated images or groups of images are used for training and testing. The results obtained on an image categorization problem comprising of 3849 images from 101 landmarks of Singapore show that (a) the procedure to automatically induce concepts from an image is effective, and (b) the use of spatial relationships, in addition to concepts, for representing an image content helps improve the classifier accuracy. This approach is the first one, to our knowledge, to present a complete extension of the language modeling approach from information retrieval to the problem of graph-based image categorization and retrieval.

Modèle de graphe et modèle de langue pour la reconnaissance de scènes visuelles.

Dans cet article, nous decrivons une methode pour utiliser un modele de langue sur des graphes pour la recherche et la categorisation d’images. Nous utilisons des regions d’images (associees automatiquement a des concepts visuels), ainsi que des relations spatiales entre ces regions, lors de la construction de la representation sous forme de graphe des images. Notre methode gere differents scenarios, selon que des images isolees ou groupees sont utilisees comme base d’apprentissage ou de test. Les resultats obtenus sur un probleme de categorisation d’images montrent (a) que la procedure automatique qui associe les concepts a une image est efficace, et (b) que l’utilisation des relations spatiales, en plus des concepts, permet d’ameliorer la qualite de la classification. Cette approche presente donc une extension du modele de langue classique en recherche d’information pour traiter le probleme de recherche et de categorisation d’images non annotees, representees par des graphes.

MRIM-LIG at ImageCLEF 2009: robotvision, image annotation and retrieval tasks

This paper describes mainly the experiments that have been conducted by the MRIM group at the LIG in Grenoble for the the ImageCLEF 2009 campaign, focusing on the work done for the Robotvision task. The proposal for this task is to study the behaviour of a generative approach inspired by the language model of information retrieval. To fit with the specificity of the Robotvision task, we added post-processing in a way to tackle with the fact that images do belong only to several classes (rooms) and that image are not independent from each others (i.e., the robot cannot in one second be in three different rooms). The results obtained still need improvement, but the use of such language model in the case of Robotvision is showed. Some results related to the Image Retrieval task and the Image annotation task are also presented.