Mircea Ionescu

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.

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

Extension of the Concept Representation in Conceptual Spaces

A compact matrix representation for concepts and instances, based on co-occurrence of properties is considered. The study builds on ideas from the theory of conceptual spaces and current work of the authors on content based image retrieval systems and image similarity measure. Initial experimental results are used to support the proposed representation

Investigating Learning Methods for Binary Data

Michie et al. show in [1] that decision trees perform better than twenty other classification algorithms in classifying binary data. In this paper we further investigate this hypothesis by comparing the decision trees with a fuzzy set-based classifier and the naive Bayes on real and artificial datasets.

Fuzzy Similarity Measure between Heterogeneous Data

Evaluating the proximity measure between heterogeneous data is very important since many datasets are heterogeneous. The application of classical proximity measures must insure a consistent meaning of proximity, by providing a consistent result across any domain with any scale. This paper investigates the application of fuzzy Hamming distance as a implementation of a component-based procedure for proximity measure.

Evaluation of various aggregation operators applied to a content image retrieval system

Fuzzy hamming distance is successfully used in a content-based image retrieval (CBIR) system as a similarity measure. The system performs an m /spl times/ n partitioning of the compared images and for each partition pairs evaluates FHD. In the last step, the FHD are defuzzified and the results are combined in a final score. In order to take full advantage of the use of fuzzy sets, the current study investigates the possibility of reversing the order of the defuzzification and aggregation steps: aggregate fuzzy set and defuzzify final result for ranking. Several t-norm and associated t-conorm aggregation operators are experimented with. The results are illustrated on retrieval operations from an image database.

Multidimensional Conceptual Spaces for Summarization

Conceptual spaces have been introduced to provide a geometrical knowledge representation. Based on it and on the fuzzy graph representation on unit space, J. T. Rickart proposed a 2-dimensional concept matrix representation across the domains used to define a concept. This paper proposes a multidimensional extension of this concept matrix.