IRIM at TRECVID2009: High Level Feature Extraction

International audienceThe IRIM group is a consortium of French teams working on Multimedia Indexing and Retrieval. This paper describes our participation to the TRECVID 2009 High Level Features detection task. We evaluated a large number of different descriptors (on TRECVID 2008 data) and tried different fusion strategies, in particular hierarchical fusion and genetic fusion. The best IRIM run has a Mean Inferred Average Precision of 0.1220, which is significantly above TRECVID 2009 HLF detection task median performance. We found that fusion of the classification scores from different classifier types improves the performance and that even with a quite low individual performance, audio descriptors can help

Mining tourist information from user-supplied collections

Tourist photographs constitute a large part of the images uploaded to photo sharing platforms. But filtering methods are needed before one can extract useful knowledge from noisy user-supplied metadata. Here we show how to extract clean trip related information (what people visit, for how long, panoramic spots) from Flickr metadata. We illustrate our technique on a sample of metadata and images covering 183 cities of different size and from different parts of the world

ThemExplorer: Finding and Browsing geo-referenced Images

Among the useful information that make browsing or finding pictures on the Web easier, geographic data take advantages from the growing amount of geo-referenced image collections and recent map-based interfaces (Google Map and Earth, Yahoo! Map, etc.). Most large scale systems for visualizing geographic entities are weakly structured (unless for commercial entities), with inhomogeneous coverage; they also make little or no use of image processing techniques in search and retrieval. In this paper, we tackle with these problems by introducing a enriched and adapted version of a geographical database and content-based facility in a new map-based visualization tool, called ThemExplorer. We present the system and evaluate different dimensions, proving its usefulness for browsing geo-referenced images. In section 1 we set up the global argument; in section 2, we discuss related work; section 3 includes an architectural overview of ThemExplorer; in Section 4, we present the contribution of our geographical database using heterogeneous sources on the Web; in section 5, we detail the CBIR techniques associated to ThemExplorer. Before concluding and presenting some future works, we describe a series of evaluation in section 6. 1

Image clustering based on a shared nearest neighbors approach for tagged collections.

International audienceBrowsing and finding pictures in large-scale and heterogeneous collections is an important issue, most particularly for online photo sharing applications. Since such services know a huge growing of their database, the tag-based indexing strategy and the results displayed in a traditional “in a single file” representation are not efficient to browse and query image collections. Naturally, data clustering appeared as a good solution by presenting a summarized view of an image set instead of an exhaustive but useless list of its element. We present a new method for image clustering based on a shared nearest neighbors approach that could be processed on both content-based features and textual descriptions (tags). We describe, discuss and evaluate the SNN method for image clustering and present some experimental results using the Flickr collections showing that our approach provides useful representations of an image set

Evaluating content based image retrieval techniques with the one million images CLIC testbed

Abstract—Pattern recognition and image recognition methods are commonly developed and tested using testbeds, which contain known responses to a query set. Until now, testbeds available for image analysis and content-based image retrieval (CBIR) have been scarce and small-scale. Here we present the one million images CEA-List Image Collection (CLIC) testbed that we have produced, and report on our use of this testbed to evaluate image analysis merging techniques. This testbed will soon be made publicly available through the EU MUSCLE Network of Excellence. Keywords—CBIR, CLIC, evaluation, image indexing and retrieval, testbed. P I

Caractérisation de l'intégrité de réseaux de neurones embarqués face aux attaques par injection de fautes Laser

International audienceLe déploiement massif des modèles de réseaux de neurones profonds sur une grande variété de plateformes matérielles a ouvert la voie à de nouvelles attaques pouvant être réalisées directement sur la surface du circuit intégré, menaçant ainsi l'intégrité et la confidentialité des réseaux de neurones embarqués.Nos travaux concernent la caractérisation de l'intégrité de réseaux de neurones, embarqués sur des microcontrôleur 32-bits, face aux attaques par injection de faute Laser. L'état de l'art montre que de faibles variations induites sur les paramètres internes du réseau de neurones (e.g., fonctions d'activation, les biais ou les poids) peuvent avoir une grande influence sur les prédictions du modèle. Pour induire de telles fautes pendant la phase d'inférence d'un modèle, nous utilisons le modèle de faute bit-set lors de la lecture de la mémoire Flash. De cette façon, nous pouvons démontrer la faisabilité d'induire un bit-set sur les valeurs des poids d'un modèle Multi Layer Perceptron (MLP) et ainsi caractériser la chute de la précision d'un modèle MNIST (classification d'images de digit). De plus, nous déterminons par simulation les bites les plus sensibles du modèle dans le but de faire chuter sa précision avec un minimum de fautes induites