Arnaud Revel

eBDtheque: A Representative Database of Comics

We present eBDtheque, a database of various comic book images and their ground truth for panels, balloons and text lines plus semantic annotations. The database consists of a hundred pages of various comic book albums, Franco-Belgian, American comics and mangas. Additionally, we present the piece of software used to establish the ground truth and a tool to validate results against this ground truth. Everything is publicly available for scientific use on http://ebdtheque.univ-lr.fr.

An application of swarm intelligence to distributed image retrieval

In this article, we introduce an application of swarm intelligence to distributed visual information retrieval distributed over networks. Based on the relevance feedback scheme, we use ant-like agents to crawl the network and to retrieve relevant images. Agents movements are influenced by markers stored on the hosts. These markers are reinforced to match the distribution of relevant images over the network. We tackle the use of the information gathered during previous search sessions. In order to match the different categories available on the network, we use several markers. Sessions searching for the same category will thus use the same makers. The system involves three learning problems: the selection of relevant markers regarding the searched category, the reinforcement of these markers and the learning of the relevance function. All of these problems are based on the relevance feedback loop. We test our system on a custom network hosting images taken from the well known TrecVid dataset. Our system shows a high improvement over classical content based image retrieval systems which do not use previous sessions information.

Image Retrieval Over Networks: Active Learning Using Ant Algorithm

In this article, we present a framework for distributed content based image retrieval with online learning based on ant-like mobile agents. Mobile agents crawl the network to find images matching a given example query. The images retrieved are shown to the user who labels them, following the classical relevant feedback scheme. The labels are used both to improve the similarity measure used for the retrieval and to learn paths leading to sites containing relevant images. The relevant paths are learned in an ethologically inspired way. We made experiments on the trecvid 2005 keyframe dataset showing that learning both the similarity function and the localization of the relevant images leads to a significant improvement. We also present an extension with the reuse of learned paths for later sessions leading to a further improvement.

Saliency filtering of SIFT detectors: application to CBIR

The recognition of object categories is one of the most challenging problems in computer vision field. It is still an open problem , especially in content based image retrieval (CBIR).When using analysis algorithm, a trade-off must be found between the quality of the results expected, and the amount of computer resources allocated to manage huge amount of generated data. In human, the mechanisms of evolution have generated the visual attention system which selects the most important information in order to reduce both cognitive load and scene understanding ambiguity. In computer science, most powerful algorithms use local approaches as bag-of-features or sparse local features. In this article, we propose to evaluate the integration of one of the most recent visual attention model in one of the most efficient CBIR method. First, we present these two algorithms and the database used to test results. Then, we present our approach which consists in pruning interest points in order to select a certain percentage of them (40% to 10%). This filtering is guided by a saliency map provided by a visual attention system. Finally, we present our results which clearly demonstrate that interest points used in classical CBIR methods can be drastically pruned without seriously impacting results. We also demonstrate that we have to smartly filter learning and training data set to obtain such results.

One gaze is worth ten thousand (key-)words

With the maturity of machine learning methods to provide satisfying Content-Based Image Retrieval systems (CBIR), research focus has recently turned back towards visual saliency analysis. The goal in these works is to extract even more efficient visual features than the existing ones. However, analyzing visual saliency is critically dependent on the task to be accomplished from the extracted visual features. A significant number of CBIR systems consider image retrieval as a binary classification problem: what is relevant for the user against what is irrelevant. In this paper, we focus on extracting relevant gaze features within the paradigm of visual preference in order to support the annotation by gaze for a CBIR system. We thus define a gaze acquisition protocol, design a benchmark from a subset of Pascal VOC database and present an in depth analysis of eye-tracking data for visual preference paradigm. Our paper provides new informations on relevant gaze features for image binary classification.

A CBIR-based evaluation framework for visual attention models

The computational models of visual attention, originally proposed as cognitive models of human attention, nowadays are being used as front-ends to numerous vision systems like automatic object recognition. These systems are generally evaluated against eye tracking data or manually segmented salient objects in images. We previously showed that this comparison can lead to different rankings depending on which of the two ground truths is used. These findings suggest that the saliency models ranking might be different for each application and the use of eye-tracking rankings to choose a model for a given application is not optimal. Therefore, in this paper, we propose a new saliency evaluation framework optimized for object recognition. This paper aims to answer the question: 1) Is the application-driven saliency models rankings consistent with classical ground truth like eye-tracking? 2) If not, which saliency models one should use for the precise CBIR applications?.

Security of Mobile Agent Platforms Using Access Control and Cryptography

Mobile Agents are autonomous software entities able to move from one host to another. However, this mobility is not all the time safe, as a hosting platform may receive agents with malicious behaviors. In this paper, we attempt to deal with this security problem by proposing a solution based on a strengthened cryptographic authentication and an access control policy. The proposed authentication process is performed through a resistant MITM Diffie-Hellman key exchange protocol, while the resources access control policy is elaborated basing an enhanced DAC model where Shamir-Threshold Scheme is used to manage and share access rights. We have conducted detailed experiments and practical investigations to evaluate the security of our approach and its effectiveness to resist face to some well known attacks.

Improvement of natural image search engines results by emotional filtering

With the Internet 2.0 era, managing user emotions is a problem that more and more actors are interested in. Historically, the first notions of emotion sharing were expressed and defined with emoticons. They allowed users to show their emotional status to others in an impersonal and emotionless digital world. Now, in the Internet of social media, every day users share lots of content with each other on Facebook, Twitter, Google+ and so on. Several new popular web sites like FlickR, Picassa, Pinterest, Instagram or DeviantArt are now specifically based on sharing image content as well as personal emotional status. This kind of information is economically very valuable as it can for instance help commercial companies sell more efficiently. In fact, with this king of emotional information, business can made where companies will better target their customers needs, and/or even sell them more products. Research has been and is still interested in the mining of emotional information from user data since then. In this paper, we focus on the impact of emotions from images that have been collected from search image engines. More specifically our proposition is the creation of a filtering layer applied on the results of such image search engines. Our peculiarity relies in the fact that it is the first attempt from our knowledge to filter image search engines results with an emotional filtering approach.

A New Hybrid Texture-Perceptual Descriptor: Application CBIR

Content based image retrieval (CBIR) has been the center of interest for a long time. A lot of research have been done to enhance the performance of these systems. Most of the proposed works focused on improving the image representation(bag-of-features) and classification methods. In this paper, we focus on enhancing the second component of CBIR system: region appearance description method. In this context, we propose a new descriptor describing the spatial frequency property of some perceptual features in the image. This descriptor has the advantage of being lower dimension vs. traditional descriptors as SIFT (60 vs. 128), thus computationally more efficient, with only 5% loss in performance using a typical CBIR algorithm on VOC 2007 dataset.

A Dynamic Scenario by Remote Supervision: A Serious Game in the Museum with a Nao Robot

This paper presents a new approach to designing and supervising an interactive experience. The approach is implemented by creating a serious game with a Nao robot. This game allows youth to discover the ethnographic artifacts of La Rochelle’s natural history museum in a playful manner. The design phase of the game is divided into two steps. The first step defined the atomic behaviors grouped within the pattern. In the second step, the agents implementing these patterns were created; they specified the contents and behaviors to be executed on the controlled process, in our case the Nao robot. The first objective is to externalize the contents of the game (e.g. robot speech) and the real behaviors of the process (e.g. the different postures and gestures of the Nao) in a database. The second objective is to be able to define a serious game without constraints on the process piloted.