SVM: Sketch-based 3D Retrieval Application Using Classification Method

Abstract

Abstract. With the rapid development of 3D technology, the demand to use and retrieve the 3D model is becoming more and more urgent. In this case, it is more and more import and necessary that sketch-based 3D model retrieval. The user provides a hand-drawn sketch, then the system can be provided the possible list of models, then the user can select the need model from this list. In this paper, it's proposed a framework of using SVM classification methods and K-means cluster algorithm. In preprocess part; the adaptive thinning algorithm is used to process the input sketch. In the offline part, the model is projected to multi-view images, and then SVM classifier has been used to classify these images. Moreover, we extract the images features, and use K-means algorithm to cluster these features. Finally, the feature dictionary will be acquired. In online process part, it only needs to extract the input sketch feature, and computes the distance between dictionary and their features. Besides, the experiment is realized to verify the feasibility of the approach. Finally, it was compared with other's approaches; the result shows that the approach is viable and robustness.