AIRCC's International Journal of Computer Science and Information Technology

My Abdellah Kassimi ¹, Omar El beqqali ¹

Affiliations
1 Sidi Mohamed Ben Abdellah University/ LIIAN, GRMS2I, Fez, MA

Abstract

The semantic-based 3D models retrieval systems have become necessary since the increase of 3D models databases. In this paper, we propose a new method for the mapping problem between 3D model data and semantic data involved in semantic based retrieval for 3D models given by polygonal meshes. First, we focused on extracting invariant descriptors from the 3D models and analyzing them to efficient semantic annotation and to improve the retrieval accuracy. Selected shape descriptors provide a set of terms commonly used to describe visually a set of objects using linguistic terms and are used as semantic concept to label 3D model. Second, spatial relationship representing directional, topological and distance relationships are used to derive other high-level semantic features and to avoid the problem of automatic 3D model annotation. Based on the resulting semantic annotation and spatial concepts, an ontology for 3D model retrieval is constructed and other concepts can be inferred. This ontology is used to find similar 3D models for a given query model. We adopted the query by semantic example approach, in which the annotation is performed mostly automatically. The proposed method is implemented in our 3D search engine (SB3DMR), tested using the Princeton Shape Benchmark Database.

Keywords

3D Model, Classification, 3D Retrieval, Semantic, Ontology, Annotation, Shape Index.

Full Text

   

Abdelghni Lakehal ¹, Omar El Beqqali ¹

Affiliations
1 Sidi Mohamed Ben Abdellah University, GRMS2I-FSDM, B.P 1796 Fez, MA

Abstract

The recent technological progress in acquisition, modeling and processing of 3D data leads to the proliferation of a large number of 3D objects databases. Consequently, the techniques used for content based 3D retrieval has become necessary. In this paper, we introduce a new method for 3D objects recognition and retrieval by using a set of binary images CLI (Characteristic level images). We propose a 3D indexing and search approach based on the similarity between characteristic level images using Hu moments for it indexing. To measure the similarity between 3D objects we compute the Hausdorff distance between a vectors descriptor. The performance of this new approach is evaluated at set of 3D object of well known database, is NTU (National Taiwan University) database.

Keywords

3D Shape Descriptor, Retrieval, 3D Zernike Moments, Hu Moments Invariants, Level Images.

Full Text