Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Latent Dirichlet Allocation (LDA) is one popular topic extraction method, which has been applied in many applications such as textual retrieval, user recommendation system and video cluster. ,  apply LDA model for visual topics extraction and utilized the topic distribution visual feature of image to handle 3D object retrieval problem. Different from the traditional LDA model,  add the spatial information of visual feature for document generation. First,  extract SIFT features from each 2D image extracted from 3D object. Then,  structure the visual documents according to the spatial information of 3D model. Finally, LDA model is used to extract the topic model for handling the retrieval problem.  further propose a multi-topic model to improve retrieval performance. Extensive comparison experiments re on the popular ETH, NTU and MV-RED 3D model datasets. The results demonstrate the superiority of the proposed method.
['Weizhi Nie', 'Xixi Li', 'Anan Liu', 'Yuting Su']
I am writing a research paper on a idea 3D object retrieval based on Spatial+LDA model
[{"from": "human", "value": "I am writing a research paper on a idea 3D object retrieval based on Spatial+LDA model"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Latent Dirichlet Allocation (LDA) is one popular topic extraction method, which has been applied in many applications such as textual retrieval, user recommendation system and video cluster. ,  apply LDA model for visual topics extraction and utilized the topic distribution visual feature of image to handle 3D object retrieval problem. Different from the traditional LDA model,  add the spatial information of visual feature for document generation. First,  extract SIFT features from each 2D image extracted from 3D object. Then,  structure the visual documents according to the spatial information of 3D model. Finally, LDA model is used to extract the topic model for handling the retrieval problem.  further propose a multi-topic model to improve retrieval performance. Extensive comparison experiments re on the popular ETH, NTU and MV-RED 3D model datasets. The results demonstrate the superiority of the proposed method."}]