2006, Bustos Cárdenas, Benjamin Eugenio, Keim, Daniel A., Saupe, Dietmar, Schreck, Tobias, Vranić, Dejan V.

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.

2004, Bustos Cárdenas, Benjamin Eugenio, Keim, Daniel A., Saupe, Dietmar, Schreck, Tobias, Vranić, Dejan V.

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.

2005, Bustos Cárdenas, Benjamin Eugenio, Keim, Daniel A., Saupe, Dietmar, Schreck, Tobias, Vranić, Dejan V.

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.

2004, Bustos Cárdenas, Benjamin Eugenio, Keim, Daniel A., Saupe, Dietmar, Schreck, Tobias, Vranić, Dejan V.

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.

2004, Bustos Cárdenas, Benjamin Eugenio, Keim, Daniel A., Saupe, Dietmar, Schreck, Tobias, Vranić, Dejan V.

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.