基于特征子空间的三维模型相关反馈检索方法

Learning Feature Manifold from User's Relevance Feedback for 3D Model Retrieval

从三维模型中提取出的高维特征向量一般分布在原始空间的某个非线性低维子空间中,而测地距度量算法可以有效地甄别这种非线性分布。提出一种在相关反馈中,通过更新测地距离来捕捉三维模型特征点的分布特性的检索算法。为了解决相关反馈后期效果提升缓慢的缺陷,作者提出了模型潜力值理论来辅助使用了测地距核函数的支持向量机学习器改善相关反馈机制。

High-dimensional feature vectors extracted from 3D models always lie on a manifold embedded in the original Euclidean space. A novel relevance feedback method using geodesic distance to discover the intrinsic manifold structure was proposed. Meanwhile, a model potential theory for revised SVM was proposed to improve the relevance feedback mechanism. Experimental results show that the approach is effective in improving the performance of content-based model retrieval systems.