arXiv subjects

Arnaud Martin

Publications and source records attributed to Arnaud Martin.

64 records · Page 4Linked to original sources

arXiv2008

Belief decision support and reject for textured images characterization

The textured images' classification assumes to consider the images in terms of area with the same texture. In uncertain environment, it could be better to take an imprecise decision or to reject the area corresponding to an unlearning class. Moreover, on the areas that are the classification units, we can have more than one texture. These considerations allows us to develop a belief decision model permitting to reject an area as unlearning and to decide on unions and intersections of learning classes. The proposed approach finds all its justification in an application of seabed characterization from sonar images, which contributes to an illustration.

cs.AI↗

arXiv2008

Experts Fusion and Multilayer Perceptron Based on Belief Learning for Sonar Image Classification

The sonar images provide a rapid view of the seabed in order to characterize it. However, in such as uncertain environment, real seabed is unknown and the only information we can obtain, is the interpretation of different human experts, sometimes in conflict. In this paper, we propose to manage this conflict in order to provide a robust reality for the learning step of classification algorithms. The classification is conducted by a multilayer perceptron, taking into account the uncertainty of the reality in the learning stage. The results of this seabed characterization are presented on real sonar images.

cs.CV↗

arXiv2008

Generalized proportional conflict redistribution rule applied to Sonar imagery and Radar targets classification

In this chapter, we present two applications in information fusion in order to evaluate the generalized proportional conflict redistribution rule presented in the chapter \cite{Martin06a}. Most of the time the combination rules are evaluated only on simple examples. We study here different combination rules and compare them in terms of decision on real data. Indeed, in real applications, we need a reliable decision and it is the final results that matter. Two applications are presented here: a fusion of human experts opinions on the kind of underwater sediments depict on sonar image and a classifier fusion for radar targets recognition.

cs.CV↗

arXiv2008

Evaluation for Uncertain Image Classification and Segmentation

Each year, numerous segmentation and classification algorithms are invented or reused to solve problems where machine vision is needed. Generally, the efficiency of these algorithms is compared against the results given by one or many human experts. However, in many situations, the location of the real boundaries of the objects as well as their classes are not known with certainty by the human experts. Furthermore, only one aspect of the segmentation and classification problem is generally evaluated. In this paper we present a new evaluation method for classification and segmentation of image, where we take into account both the classification and segmentation results as well as the level of certainty given by the experts. As a concrete example of our method, we evaluate an automatic seabed characterization algorithm based on sonar images.

cs.CV↗

arXiv2008

A new generalization of the proportional conflict redistribution rule stable in terms of decision

In this chapter, we present and discuss a new generalized proportional conflict redistribution rule. The Dezert-Smarandache extension of the Demster-Shafer theory has relaunched the studies on the combination rules especially for the management of the conflict. Many combination rules have been proposed in the last few years. We study here different combination rules and compare them in terms of decision on didactic example and on generated data. Indeed, in real applications, we need a reliable decision and it is the final results that matter. This chapter shows that a fine proportional conflict redistribution rule must be preferred for the combination in the belief function theory.

cs.AI↗

arXiv2008

Human expert fusion for image classification

In image classification, merging the opinion of several human experts is very important for different tasks such as the evaluation or the training. Indeed, the ground truth is rarely known before the scene imaging. We propose here different models in order to fuse the informations given by two or more experts. The considered unit for the classification, a small tile of the image, can contain one or more kind of the considered classes given by the experts. A second problem that we have to take into account, is the amount of certainty of the expert has for each pixel of the tile. In order to solve these problems we define five models in the context of the Dempster-Shafer Theory and in the context of the Dezert-Smarandache Theory and we study the possible decisions with these models.

cs.CV↗

arXiv2008

Une nouvelle règle de combinaison répartissant le conflit - Applications en imagerie Sonar et classification de cibles Radar

These last years, there were many studies on the problem of the conflict coming from information combination, especially in evidence theory. We can summarise the solutions for manage the conflict into three different approaches: first, we can try to suppress or reduce the conflict before the combination step, secondly, we can manage the conflict in order to give no influence of the conflict in the combination step, and then take into account the conflict in the decision step, thirdly, we can take into account the conflict in the combination step. The first approach is certainly the better, but not always feasible. It is difficult to say which approach is the best between the second and the third. However, the most important is the produced results in applications. We propose here a new combination rule that distributes the conflict proportionally on the element given this conflict. We compare these different combination rules on real data in Sonar imagery and Radar target classification.

cs.AI↗

arXiv2008

Toward a combination rule to deal with partial conflict and specificity in belief functions theory

We present and discuss a mixed conjunctive and disjunctive rule, a generalization of conflict repartition rules, and a combination of these two rules. In the belief functions theory one of the major problem is the conflict repartition enlightened by the famous Zadeh's example. To date, many combination rules have been proposed in order to solve a solution to this problem. Moreover, it can be important to consider the specificity of the responses of the experts. Since few year some unification rules are proposed. We have shown in our previous works the interest of the proportional conflict redistribution rule. We propose here a mixed combination rule following the proportional conflict redistribution rule modified by a discounting procedure. This rule generalizes many combination rules.

cs.AI↗

arXiv2008

Fusion for Evaluation of Image Classification in Uncertain Environments

We present in this article a new evaluation method for classification and segmentation of textured images in uncertain environments. In uncertain environments, real classes and boundaries are known with only a partial certainty given by the experts. Most of the time, in many presented papers, only classification or only segmentation are considered and evaluated. Here, we propose to take into account both the classification and segmentation results according to the certainty given by the experts. We present the results of this method on a fusion of classifiers of sonar images for a seabed characterization.

cs.AI↗

arXiv2008

Decision Support with Belief Functions Theory for Seabed Characterization

The seabed characterization from sonar images is a very hard task because of the produced data and the unknown environment, even for an human expert. In this work we propose an original approach in order to combine binary classifiers arising from different kinds of strategies such as one-versus-one or one-versus-rest, usually used in the SVM-classification. The decision functions coming from these binary classifiers are interpreted in terms of belief functions in order to combine these functions with one of the numerous operators of the belief functions theory. Moreover, this interpretation of the decision function allows us to propose a process of decisions by taking into account the rejected observations too far removed from the learning data, and the imprecise decisions given in unions of classes. This new approach is illustrated and evaluated with a SVM in order to classify the different kinds of sediment on image sonar.

cs.AI↗