3D object retrieval based on histogram of local orientation using one-shot score support vector machine

Abstract

In this paper, a content based descriptor is proposed to retrieve 3D models, which employs histogram of local orientation (HLO) as a geometric property of the shape. The proposed 3D model descriptor scheme consists of three steps. In the first step, Poisson equation is utilized to define a 3D model signature. Next, the local orientation is calculated for each voxel of the model using Hessian matrix. As the final step, a histogram-based 3D model descriptor is extracted by accumulating the values of the local orientation in bins. Due to efficiency of Poisson equation in describing the models with various structures, the proposed descriptor is capable of discriminating these models accurately. Since, the inner voxels have a dominant contribution in the formation of the descriptor, sufficient robustness against noise can be achieved. This is because the noise mostly influences the boundary voxels. Furthermore, we improve the retrieval performance using support vector machine based one-shot score (SVM-OSS) similarity measure, which is more efficient than the conventional methods to compute the distance of feature vectors. The rotation normalization is performed employing the principal component analysis. To demonstrate the applicability of HLO, we implement experimental evaluations of precisionrecall curve on ESB, PSB and WM-SHREC databases of 3D models. Experimental results validate the effectiveness of the proposed descriptor compared to some current methods.

Keywords

3D model retrieval / histogram of local orientation / visual based shape descriptor / poisson equation

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