An improved content based image retrieval in RGBD images using Point Clouds

Content-based image retrieval (CBIR) system helps users to retrieve images based on their contents. Therefore, a reliable CBIR method is required to extract important information from the image. This important information includes texture, color, shape of the object in the image etc. For RGBD images, the 3D surface of the object is the most important feature. We propose a new algorithm which recognize the 3D object by using 3D surface shape features, 2D boundary shape features, and the color features. We present an efficient method for 3D object shape extraction. For that we are using first and second order derivatives over the 3D coordinates of point clouds for detecting landmark points on the surface of RGBD object. Proposed algorithm identifies the 3D surface shape features efficiently. For the implementation we use Point Cloud Library(PCL). Experimental results show that the proposed method is effective and efficient and it is able to give more than 80% classification rate for any objects in our test data. Also it eliminates false positive results and it yields higher retrieval accuracy.