View based 3D object retrieval using local binary and ternary pattern

In this paper, we present a new combination view based feature descriptors for 3D textured retrieval system when models vary either by geometric shape or texture or both. First, we perform pose normalization to modify arbitrary 3D objects in order to have same orientation. We then do projection to the 3D objects into images. This purposes to make all the 3D objects have the same size. Through this image, we can capture information about shape, texture and color. First, we build local binary pattern histogram and color image histogram. Second, we build local ternary pattern histogram. Then we also compute Fourier transform in spatial space. Finally, we merge all features by linear combination. For experiment, we use standard evaluation measures such as Nearest Neighbor (NN), First Tier (FT), Second Tier (ST), Average Dynamic Recall (ADR). Dataset in SHREC 2014 and its evaluation program is used to verify the proposed method. Experiment result show that the proposed method is more accurate when compared with some methods of state-of-the-art.