View-Clustering and Manifold Learning for Sketch-Based 3D Model Retrieval

Retrieval of 3D models by using 2D sketch query has recently become an active area of research. However, retrieval accuracy of the modality is still much lower than using a 3D model example as query. In this paper, we propose an algorithm that employs manifold-learning based dimension reduction for sketch-based 3D model retrieval. The algorithm compares multi-view rendering of 3D models with the 2D sketch. To improve distance computation, a distance metric adapted to data sample distribution is learned by using a manifold-learning algorithm, that is, Locally Linear Embedding (LLE). In order to lower the cost of training the LLE, we reduce number of training samples by clustering, either in feature space or in view space. Experimental evaluation has shown that both view space clustering and feature space clustering lowers training cost by more than 10 times while significantly improving retrieval accuracy. A compact 50 dimensional feature after the dimension reduction is much faster to compare, and its retrieval accuracy is 40% better than the original 30k dimensional feature. In terms of training cost, view clustering approach costs 20 times less than the one using full set of features without clustering.