Conference Paper

# A New Feature Integration Approach and Its Application to 3D Model Retrieval

Dept. of Comput. Sci. & Inf. Eng., Chung Hua Univ., Hsinchu, Taiwan

DOI: 10.1109/IIH-MSP.2009.255 Conference: Intelligent Information Hiding and Multimedia Signal Processing, 2009. IIH-MSP '09. Fifth International Conference on Source: IEEE Xplore

- Citations (14)
- Cited In (0)

- [Show abstract] [Hide abstract]

**ABSTRACT:**This paper presents a novel methodology for content-based search and retrieval of 3D objects. After proper positioning of the 3D objects using translation and scaling, a set of functionals is applied to the 3D model producing a new domain of concentric spheres. In this new domain, a new set of functionals is applied, resulting in a descriptor vector which is completely rotation invariant and thus suitable for 3D model matching. Further, weights are assigned to each descriptor, so as to significantly improve the retrieval results. Experiments on two different databases of 3D objects are performed so as to evaluate the proposed method in comparison with those most commonly cited in the literature. The experimental results show that the proposed method is superior in terms of precision versus recall and can be used for 3D model search and retrieval in a highly efficient manner.EURASIP Journal on Advances in Signal Processing. 01/2007; - [Show abstract] [Hide abstract]

**ABSTRACT:**In recent years many techniques for 3D shape retrieval and classification were proposed. Most of them follow the feature vector paradigm, i.e. the shape is extended with some compact feature representation, on which basis the objects are compared and similarity measures are computed. A main demand of such similarity measures is their invariance to Euclidean motion. There are three main direction to obtain such invariances: Matched Filter Approaches, Pose Normalization or Transformation Group Integration. Among those, registration approaches perform best in retrieval accuracy, while their computational expense however is rather high. In contrast, representation obtained by group integration are fast to compute and compare, but show bad retrieval performance due to its loss in information. In this article we try to close this gap and show that it is also possible to obtain meaningful representations of surface models by group integration approaches.Computers & Graphics. 01/2006; - [Show abstract] [Hide abstract]

**ABSTRACT:**In molecular databases, structural classification is a basic task that can be successfully approached by nearest neighbor methods. The underlying similarity models consider spatial properties such as shape and extension as well as thematic attributes. We introduce 3D shape histograms as an intuitive and powerful approach to model similarity for solid objects such as molecules. Errors of measurement, sampling, and numerical rounding may result in small displacements of atomic coordinates. These effects may be handled by using quadratic form distance functions. An efficient processing of similarity queries based on quadratic forms is supported by a filter-refinement architecture. Experiments on our 3D protein database demonstrate the high classification accuracy of more than 90% and the good performance of the technique.Proceedings / ... International Conference on Intelligent Systems for Molecular Biology; ISMB. International Conference on Intelligent Systems for Molecular Biology 02/1999;

Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.