Multiview Semi-Supervised Ranking for Automatic Image Annotation

International audienceMost photo sharing sites give their users the opportunity to manually label images. The labels collected that way are usually very incomplete due to the size of the image collections: most images are not labeled according to all the categories they belong to, and, conversely, many class have relatively few representative examples. Automated image systems that can deal with small amounts of labeled examples and unbalanced classes are thus necessary to better organize and annotate images. In this work, we propose a multiview semi-supervised bipartite ranking model which allows to leverage the information contained in unlabeled sets of images in order to improve the prediction performance, using multiple descriptions, or views of images. For each topic class, our approach first learns as many view-specific rankers as available views using the labeled data only. These rankers are then improved iteratively by adding pseudo-labeled pairs of examples on which all view-specific rankers agree over the ranking of examples within these pairs. We report on experiments carried out on the NUS-WIDE dataset, which show that the multiview ranking process improves predictive performances when a small number of labeled examples is available specially for unbalanced classes. We show also that our approach achieves significant improvements over a state-of the art semi-supervised multiview classification model

Ultra-Fast, High-Performance 8x8 Approximate Multipliers by a New Multicolumn 3,3:2 Inexact Compressor and its Derivatives

Multiplier, as a key role in many different applications, is a time-consuming, energy-intensive computation block. Approximate computing is a practical design paradigm that attempts to improve hardware efficacy while keeping computation quality satisfactory. A novel multicolumn 3,3:2 inexact compressor is presented in this paper. It takes three partial products from two adjacent columns each for rapid partial product reduction. The proposed inexact compressor and its derivates enable us to design a high-speed approximate multiplier. Then, another ultra-fast, high-efficient approximate multiplier is achieved utilizing a systematic truncation strategy. The proposed multipliers accumulate partial products in only two stages, one fewer stage than other approximate multipliers in the literature. Implementation results by Synopsys Design Compiler and 45 nm technology node demonstrates nearly 11.11% higher speed for the second proposed design over the fastest existing approximate multiplier. Furthermore, the new approximate multipliers are applied to the image processing application of image sharpening, and their performance in this application is highly satisfactory. It is shown in this paper that the error pattern of an approximate multiplier, in addition to the mean error distance and error rate, has a direct effect on the outcomes of the image processing application.Comment: 21 Pages, 18 Figures, 6 Table

Semi-supervised multi-view learning (an application to image annotation and multi-lingual document classification)

In this thesis, we introduce two multiview learning approaches. In a first approach, we describe a self-training multiview strategy which trains different voting classifiers on different views. The margin distributions over the unlabeled training data, obtained with each view-specific classifier are then used to estimate an upper-bound on their transductive Bayes error. Minimizing this upper-bound provides an automatic margin-threshold which is used to assign pseudo-labels to unlabeled examples. Final class labels are then assigned to these examples, by taking a vote on the pool of the previous pseudo-labels. New view-specific classifiers are then trained using the original labeled and the pseudo-labeled training data. We consider applications to image-text and to multilingual document classification.In second approach, we propose a multiview semi-supervised bipartite ranking model which allows us to leverage the information contained in unlabeled sets of images to improve the prediction performance, using multiple descriptions, or views of images. For each topic class, our approach first learns as many view-specific rankers as there are available views using the labeled data only. These rankers are then improved iteratively by adding pseudo-labeled pairs of examples on which all view-specific rankers agree over the ranking of examples within these pairs.Dans cette thèse , nous présentons deux méthodes d'apprentissage Multi-vues . Dans une première approche , nous décrivons une stratégie de multi-vues auto-apprentissage qui apprends différents classifieurs de vote sur les différents points de vue. Les distributions de marge sur les données d'apprentissage vierge, obtenus avec chaque classifieur spécifique à la vue sont ensuite utilisées pour estimer une borne supérieure de leur erreur de Bayes transductive. Minimiser cette borne supérieure nous donne une marge de seuil automatique qui est utilisé pour attribuer des pseudo-labels à des exemples non étiquetés. Étiquettes pour les classes finales sont ensuite affectés à ces exemples, par un vote à l'ensemble de la précédente pseudo -labels . Nouveaux classifieurs vue spécifiques sont ensuite apprises à l'aide des données d'apprentissage pseudo- étiquetés et les données étiquetées l'original. Nous considérons applications à l'image-texte et la classification de documents multilingues. Dans la deuxième approche , nous proposons un modèle du ranking bipartite semi-supervisé multivues qui nous permet de tirer parti de l'information contenue dans ensembles non-étiquetées d'images pour améliorer les performances de prédiction , en utilisant plusieurs descriptions ou des vues d'images. Pour chaque catégorie de sujet , notre approche apprend d'abord autant rankers spécifique à la vue qu'il ya de vues disponibles en utilisant les données étiquetées seulement. Ces rankers sont ensuite améliorées itérativement en ajoutant paires d'exemples pseudo- étiquetés sur lesquels tous les rankers spécifiques à la vue sont d'accord sur le classement des exemples au sein de ces couples.PARIS-BIUSJ-Mathématiques rech (751052111) / SudocSudocFranceF

UPMC/LIP6 at ImageCLEFannotation 2009: Large Scale Visual Concept Detection and Annotation

International audienc

UPMC/LIP6 at ImageCLEFannotation 2010

International audienceIn this paper, we present the LIP6 annotation models for the ImageCLEFannotation 2010 task. We study two methods to train and merge the results of different classifiers in order to improve annotation. In particular, we propose a multiview learning model based on a RankingSVM. We also consider the use of the tags matching the visual concept names to improve the scores predicted by the models. The experiments show the difficulty of merging several classifiers and also the interest to have a robust model able to merge relevant information. Our method using tags always improves the results

UPMC/LIP6 at ImageCLEFannotation 2009: Large Scale Visual Concept Detection and Annotation

International audienc

Multiview self-learning

In many applications, observations are available with different views. This is, for example, the case with image-text classification, multilingual document classification or document classification on the web. In addition, unlabeled multiview examples can be easily acquired, but assigning labels to these examples is usually a time consuming task. We describe a multiview self-learning strategy which trains different voting classifiers on different views. The margin distributions over the unlabeled training data, obtained with each view-specific classifier are then used to estimate an upper-bound on their transductive Bayes error. Minimizing this upper-bound provides an automatic margin-threshold which is used to assign pseudo-labels to unlabeled examples. Final class labels are then assigned to these examples, by taking a vote on the pool of the previous pseudo-labels. New view-specific classifiers are then trained using the labeled and pseudo-labeled training data. We consider applications to image-text classification and to multilingual document classification. We present experimental results on the NUS-WIDE collection and on Reuters RCV1-RCV2 which show that despite its simplicity, our approach is competitive with other state-of-the-art techniques.Peer reviewed: YesNRC publication: Ye

UPMC/LIP6 at ImageCLEF's WikipediaMM: An Image-Annotation Model for an Image Search-Engine

International audienceIn this paper, we present the LIP6 retrieval system which automatically ranks the most similar images to a given query constituted of both textual and/or visual information through a given textual-visual collection. The system first preprocesses the data set in order to remove stop-words as well as non-informative terms. For each given query, it then finds a ranked list of its most similar images using only their textual informations. Visual features are then used to obtain a second ranking list from a manifold and a linear combination of these two ranking lists gives the final ranking of images

Exploitation du contenu visuel pour améliorer la recherche textuelle d'images en ligne

National audienceLes moteurs de recherche d'images sur le web utilisent principalement l'information textuelle associée aux images afin de retrouver les images pertinentes, tandis que le contenu visuel, moins sémantique et plus coûteux en temps de calcul, est très peu utilisé dans la phase « en ligne ». Nous proposons une chaîne de traitements complète proposant deux façons efficaces et peu coûteuses d'utiliser le contenu visuel des images dans la phase en ligne. La première façon propose d'améliorer la précision des résultats retrouvés en filtrant les résultats textuels en fonction des concepts visuels détectés dans la requête textuelle. Pour cela, nous apprenons les concepts visuels à l'aide de forêts d'arbres de décision flous. Ce travail montre une nette amélioration des résultats lorsque l'on utilise les concepts apparaissant explicitement dans la requête. La deuxième façon propose d'améliorer la diversité des résultats pertinents obtenus afin de mieux satisfaire le besoin d'information de l'utilisateur. Pour cela, nous utilisons un partitionnement de l'espace visuel. Nous montrons que cette approche est effectivement efficace