Schreck, Tobias
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An Experimental Effectiveness Comparison of Methods for 3D Similarity Search
Methods for content-based similarity search are fundamental for managing large multimedia repositories, as they make it possible to conduct queries for similar content, and to organize the repositories into classes of similar objects. 3D objects are an important type of multimedia data with many promising application possibilities. Defining the aspects that constitute the similarity among 3D objects, and designing algorithms that implement such similarity definitions is a difficult problem. Over the last few years, a strong interest in 3D similarity search has arisen, and a growing number of competing algorithms for the retrieval of 3D objects have been proposed. The contributions of this paper are to survey a body of recently proposed methods for 3D similarity search, to organize them along a descriptor extraction process model, and to present an extensive experimental effectiveness and efficiency evaluation of these methods, using several 3D databases.
2D Maps for Visual Analysis and Retrieval in Large Multi-Feature 3D Model Databases
Multimedia objects are often described by high-dimensional feature vectors which can be used for retrieval and clustering tasks. We have built an interactive retrieval system for 3D model databases that implements a variety of different feature transforms. Recently, we have enhanced the functionality of our system by integrating a SOM-based visualization module. In this poster demo, we show how 2D maps can be used to improve the effectiveness of retrieval, clustering, and over-viewing tasks in a 3D multimedia system.
Using Entropy Impurity for Improved 3D Object Similarity Search
Similarity search in 3D object databases is becoming an important problem in multimedia retrieval, with many practical applications. We investigate methods for improving the effectiveness in a retrieval system that implements multiple feature extraction algorithms to choose from. Our techniques are based on the entropy impurity measure, widely used in the context of decision trees. We propose a method for the a priori estimation of individual feature vector performance given a query. We then define two approaches that use this estimator to improve the retrieval effectiveness. Our experimental results show that significant improvements are achievable using these methods.
Foundations of 3D Digital Libraries : current approaches and urgent research challenges
3D documents are an indispensable data type in many important application domains such as Computer Aided Design, Simulation and Visualization, and Cultural Heritage, to name a few. The 3D document type can represent arbitrarily complex information by composing geometrical, topological, structural, or material properties, among others. It often is integrated with meta data and annotation by the various application systems that produce, process, or consume 3D documents. We argue that due to the inherent complexity of the 3D data type in conjunction with and imminent pervasive usage and explosion of available content, there is pressing need to address key problems of the 3D data type. These problems need to be tackled before the 3D data type can be fully supported by Digital Library technology in the sense of a generalized document, unlocking its full potential. If the problems are addressed appropriately, the expected benefits are manifold and may lead to radically improved production, processing, and consumption of 3D content.
A pivot-based index structure for combination of feature vectors
We present a novel indexing schema that provides efficient nearest-neighbor queries in multimedia databases consisting of objects described by multiple feature vectors. The benefits of the simultaneous usage of several (statically or dynamically) weighted feature vectors with respect to retrieval effectiveness have been previously demonstrated. Support for efficient multi-feature vector similarity queries is an open problem, as existing indexing methods do not support dynamically parameterized distance functions. We present a solution for this problem relying on a combination of several pivot-based metric indices. We define the index structure, present algorithms for performing nearest-neighbor queries on these structures, and demonstrate the feasibility by experiments conducted on two real-world image databases. The experimental results show a significant performance improvement over existing access methods.
Automatic Selection and Combination of Descriptors for Effective 3D Similarity Search
We focus on improving the effectiveness of similarity search in 3D object repositories from a system-oriented perspective. Motivated by an effectiveness evaluation of several individual 3D retrieval methods, we research a selection heuristic, called purity, for choosing retrieval methods based on query-dependent characteristics. We show that the purity selection method significantly improves the search effectiveness compared to the best single methods. We then show that retrieval effectiveness can be further boosted by considering combinations of multiple retrieval methods to perform the search. We propose to use a dynamically weighted combination of feature vectors based on the purity concept, and we experimentally show that the search effectiveness of our combined methods by far exceeds the effectiveness of our best implemented single method.
Methoden und Benutzerschnittstellen für effektives Retrieval in 3D-Datenbanken
3D Objekte sind ein wichtiger Typ Multimedia Daten mit einer Reihe vielversprechender Anwendungsmöglichkeiten etwa in der industriellen Produktion, in Simulation, Unterhaltung und Visualisierung. Die Definition von Ähnlichkeit zwischen 3D Objekten und die Implementierung von entsprechenden Ähnlichkeitssuchalgorithmen sind interessant für den Einsatz in 3D-Datenbanksystemen, repräsentieren aber gleichzeitig schwierige Probleme. In dieser Arbeit stellen wir Methoden dar, um effektives Retrieval in 3D-Datenbanken zu realisieren. Wir besprechen zudem Methoden, um Ergebnisse von Ähnlichkeitssuchanfragen sowie ganze 3D Objekträume visuell zu analysieren.
Feature-based Similarity Search in 3D Object Databases
The development of effective content-based multimedia search systems is an important research issue, due to the growing amount of digital audio-visual information. In the case of images and video, the growth of digital data has been observed since the introduction of 2D capture devices. A similar development is expected for 3D data, as acquisition and dissemination technology of 3D models is constantly improving. 3D objects are becoming an important type of multimedia data, with many promising application possibilities. Defining the aspects that constitute the similarity among 3D objects, and designing algorithms that implement such similarity definitions, is a difficult problem. Over the last few years, a strong interest in methods for 3D similarity search has arisen, and a growing number of competing algorithms for content-based retrieval of 3D objects have been proposed. We survey feature-based methods for 3D retrieval, and we propose a taxonomy for these methods. We also present experimental results, comparing the effectiveness of some of the surveyed methods.
An Experimental Comparison of Feature-Based 3D Retrieval Methods
3D objects are an important type of multimedia data with many promising application possibilities. Defining the aspects that constitute the similarity among 3D objects, and designing algorithms that implement such similarity definitions is a difficult problem. Over the last few years, a strong interest in methods for feature-based 3D similarity search has arisen, and a growing number of competing algorithms for content-based retrieval of 3D objects have been proposed. We present an extensive experimental evaluation of the retrieval effectiveness and efficiency of a large part of the current state-of-the-art feature-based methods for 3D similarity search, giving a contrasting assessment of the different approaches.