Anca L. Ralescu

The Effect of Imbalanced Data Class Distribution on Fuzzy Classifiers - Experimental Study

This study evaluates the robustness of a fuzzy classifier when class distribution of the training set varies. The analysis of the results is based on the classification accuracy and ROC curves. The experimental results reported here show that fuzzy classifiers are less variant with the class distribution and less sensitive to the imbalance factor than decision trees

Fuzzy hamming distance in a content-based image retrieval system

The performance of content-based image retrieval (CBIR) systems mainly depends on the image similarity measure that it uses. The fuzzy Hamming distance (D) is an extension of the Hamming distance for real-valued vectors. Because the feature space of each image is real-valued, the fuzzy Hamming distance can be successfully used as an image similarity measure. The current study reports on the results of applying D as a similarity measure between the color histograms of two images. The fuzzy Hamming distance is suitable for this application because it can take into account not only the number of different colors but also the magnitude of this difference.

Some issues in fuzzy and linguistic modeling

We discuss some issues arising in fuzzy logic based quantitative modeling. A linguistic model is a fuzzy in which the fuzzy sets are labelled, usually by elements of a dictionary. The issues presented are of general interest, and for illustration we refer to our previous work (1994) on facial expression modeling.<<ETX>>

Modeling of natural objects including fuzziness and application to image understanding

Presents an object recognition method which takes into account fuzziness both in the object model and the matching operation between the model and the image processing results. Objects are described by a hierarchical model. Attributes of components such as color, shape, and size are described by fuzzy sets. The importance and correlation between attributes and components are described using a fuzzy measure. Methods of determining this fuzzy measure are examined. Considering the relative importance and these correlations, objects are recognized by integrating the outcome of the matching between the results of image processing and the attributes of the model. At the experimental level, the method was applied to recognition of an abstract painting and a real photograph.<<ETX>>

Fuzzy Hamming Distance Based Banknote Validator

Banknote validation systems are used to discriminate between genuine and counterfeit banknotes. The paper proposes a one-class classifier for genuine class using a new similarity measure based on the fuzzy Hamming distance. For each banknote several regions are considered (corresponding to security features) and each region is split in m times n partitions, to include position information. The feature space used by the classifier consists of color histograms of each partition. The fuzzy Hamming distance proves to have a good discrimination power being able to completely discriminate between the genuine and counterfeit banknotes

Fuzzy perceptual grouping in image understanding

The image understanding work we present is part of a navigation support system. We explore the use of fuzzy techniques for image understanding via perceptual organization. A brief review of previous work on perceptual organization introduces our motivation for using fuzzy techniques for representation which in turns entails their use for reasoning about the result of the organization. The approach is supported by experimental results obtained for an office scene environment.<<ETX>>

Towards a multimedia model-based image retrieval system using fuzzy logic

This paper describes our current research on the subject of image retrieval based on a model of the image data. The work focuses on retrieving the image of a natural object from a collection of like images; the application considered is that of retrieval from a data base of face photographs. The difficulty arises mainly from the fact that precise, geometric models of natural images are not necessarily available. The work described builds on previous work on an image retrieval system based on linguistic modeling of image data using fuzzy sets: such a model is a collection of statements `X is F, where X refers to a region/component of the object and F refers to a fuzzy set, such as `big, `small, `long, which describes the size of X. In this paper we consider the situation when the model of the image is expressed in a graphic form (sketch). The motivation for considering this subject arises, among others, from: (1) the fact that not all image characteristics (such as a wrinkle for example) can be described linguistically, (2) linguistic models tend to be subjective, and (3) a linguistic model may not be available. Retrieval based on mixed queries, linguistic and graphic, is considered. Fuzzy logic is used to express the linguistic model of the image data, and for reasoning.

Fuzzy logic approach to model-based image analysis

In this paper, we propose a model representation of objects including fuzziness and a matching method between the model and the result of features extraction from image data. In the model representation, the objects are described hierarchically and the attributes of each component such as color, shape, and size are described by fuzzy sets. The ambiguous correlations between attributes and components are described using fuzzy measure. Considering the correlations, the objects are recognized by integrating the results of the matching between the results of image processing and the attributes in the model. Finally, the method is applied to recognition of a real photograph as experimental results and their effect are discussed.

From And to Or

In this paper we consider the issue of aggregation of predicates. This issue appears especially in connection with fuzzy predicates. We give a probabilistic interpretation of an aggregation in terms of the possibility of a statement containing these predicates, and using “and”/“or” operators.

Computational model of perceptual information processing

We study an intelligent information processing method based on users perception, named perceptual information processing (PIP). This method consists of the following tasks: (a) acquisition of users perception from examples, (b) recognition by sensing, and (c) refinement of the perception based on the recognition result. Fuzzy sets are used to represent perceptions and a soft computing method called mass assignment theory is used as PIPs computational core. The paper introduces the computational model of PIP and discusses the issues that arise in connection with this model.