Characterizing Image Sets Using Formal Concept Analysis
- PDF / 618,583 Bytes
- 8 Pages / 600 x 792 pts Page_size
- 45 Downloads / 231 Views
Characterizing Image Sets Using Formal Concept Analysis Emmanuel Zenou National School of Aeronautics and Space (SUPAERO), 10 Edouard Belin Avenue, BP 54032, 31055 Toulouse Cedex, France LAAS -CNRS, 7 Colonel Roche Avenue, 31077 Toulouse Cedex 4, France Email: [email protected]
Manuel Samuelides National School of Aeronautics and Space (SUPAERO), 10 Edouard Belin Avenue, BP 54032, 31055 Toulouse Cedex, France Email: [email protected] Received 29 December 2003; Revised 5 September 2004 This article presents a new method for supervised image classification. Given a finite number of image sets, each set corresponding to a place of an environment, we propose a localization strategy, which relies upon supervised classification. For each place, the corresponding landmark is actually a combination of features that have to be detected in the image set. Moreover, these features are extracted using a symbolic knowledge extraction theory, “formal concept analysis.” This paper details the full landmark extraction process and its hierarchical organization. A real localization problem in a structured environment is processed as an illustration. This approach is compared with an optimized neural network-based classification, and validated with experimental results. Further research to build up hybrid classifier is outlined in the discussion. Keywords and phrases: supervised classification, visual landmarks, Galois lattices, concept lattices, computer vision, localization.
1.
INTRODUCTION
Characterizing and recognizing a place in a structured or not environment, using only a set of views attached to each place to characterize, is a difficult challenge to take up for a machine (computer or robot) today. To do this, the machine needs to find “something” that (1) characterizes a considered place, and (2) distinguishes it from the others. This “something,” under specific conditions, is called a (visual) landmark. What is a landmark? How to find it? And how to select it? This paper presents a new method to answer these questions. All the images issued from one place are regrouped into a set. Thus, the machine has to recognize one original place from some images of the associated set. At first, during a learning stage, the relationships between sets of images and features are structured and organized into a hierarchy, through a formalism called Galois lattices, or concept lattices. The use of such mathematical structures allows the machine to determine its own landmarks attached to each place. Subsequently, once this initial characterization has been performed, the machine is able in a second stage to recognize the corresponding place thanks to the landmarks it has learned. The choice of the application we have done makes the connection between one set of images and one room of a
structured environment. Thus we expect that there will be more or less common properties between images of one set. But the theory we have developed here considers only sets of images without any restriction. This paper is organized as follows. Secti
Data Loading...
