Distance Metric Learning Based on Semantic Correlation Strength for 3D Model Retrieval

3D model retrieval is an important part of multimedia information retrieval. To overcome the drawbacks of traditional text-based method, current researches mainly concentrate on the content-based 3D model retrieval. However, the effect of the content-based method is not satisfactory because of the semantic gap. Therefore, we propose a new 3D model retrieval method using semantic-correlation-strength-based distance metric learning. The method firstly obtains semantic correlation strength between 3D models from users´ long-term relevance feedbacks, then uses semantic correlation strength as weights and adopts improved weighted relevant component analysis method to learn a Mahalanobis distance function. Finally, using the learned Mahalanobis distance metric function to retrieve 3D models. Experiments on Princeton Shape Benchmark show the effectiveness of our proposed method.