Euripides G. M. Petrakis

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We propose a method to handle approximate searching by image content in medical image databases. Image content is represented by attributed relational graphs holding features of objects and relationships between objects. The method relies on the assumption that a fixed number of “labeled” or “expected” objects (e.g., “heart”, “lungs” etc.) are common in all(More)
Semantic Similarity relates to computing the similarity between concepts which are not lexicographically similar. We investigate approaches to computing semantic similarity by mapping terms (concepts) to an ontology and by examining their relationships in that ontology. Some of the most popular semantic similarity methods are implemented and evaluated using(More)
We propose an approach for matching distorted and possibly occluded shapes using Dynamic Programming (DP). We distinguish among various cases of matching such as cases where the shapes are scaled with respect to each other and cases where an open shape matches the whole or only a part of another open or closed shape. Our algorithm treats noise and shape(More)
Semantic Similarity relates to computing the similarity between concepts (terms) which are not necessarily lexically similar. We investigate approaches to computing semantic similarity by mapping terms to an ontology and by examining their relationships in that ontology. More specifically, we investigate approaches to computing the semantic similarity(More)
We present iCluster, a self-organizing peer-to-peer overlay network for supporting full-fledged information retrieval in a dynamic environment. iCluster works by organizing peers sharing common interests into clusters and by exploiting clustering information at query time for achieving low network traffic and high recall. We define the criteria for peer(More)
We propose a shape matching algorithm for deformed shapes based on dynamic programming. Our algorithm is capable of grouping together segments at finer scales in order to come up with appropriate correspondences with segments at coarser scales. We illustrate the effectiveness of our algorithm in retrieval of shapes by content on two different(More)
This work addresses issues related to the design and implementation of focused crawlers. Several variants of state-of-the-art crawlers relying on web page content and link information for estimating the relevance of web pages to a given topic are proposed. Particular emphasis is given to crawlers capable of learning not only the content of relevant pages(More)
Similarity retrieval by spatial image content (i.e., using multiple objects and their relationships in space) is an open problem which has received considerable attention in the literature. The most powerful approaches of spatial image content representation and retrieval are “Attributed Relational Graphs” (ARGs) and “Symbolic Projections” (e.g., 2D(More)
We introduce ImageMap, as a method for indexing and similarity searching in Image DataBases (IDBs). ImageMap answers “queries by example,” involving any number of objects or regions and taking into account their interrelationships. We adopt the most general image content representation, that is, Attributed Relational Graphs (ARGs), in conjunction with the(More)