Hasan Farsi - Department of Electrical and Computer Engineering, Birjand University, Birjand, Iran
Reza Nasiripour - Department of Electrical, Faculty of Engineering, Birjand University, Birjand, Iran
Sajjad Mohammadzadeh - Department of Electrical, Faculty of Engineering, Birjand University, Birjand, Iran

Normally, the-state-of-the-art methods in field of object retrieval for large databases are achieved by training process. We propose a novel large-scale generic object retrieval which only uses a single query image and training-free. Current object retrieval methods require a part of image database for training to construct the classifier. This training can be supervised or unsupervised and semi-supervised. In the proposed method, the query image can be a typical real image of the object. The object is constructed based on Speeded Up Robust Features (SURF) points acquired from the image. Information of relative positions, scale and orientation between SURF points are calculated and constructed into the object model. Dynamic programming is used to try all possible combinations of SURF points for query and datasets images. The ability to match partial affine transformed object images comes from the robustness of SURF points and the flexibility of the model. Occlusion is handled by specifying the probability of a missing SURF point in the model. Experimental results show that this matching technique is robust under partial occlusion and rotation. The properties and performance of the proposed method are demonstrated on the large databases. The average of retrieval rate by the proposed method applied on Oxford landmarks and Corel dataset are 69.68% and 65.79%, respectively. Also, the average of ANMRR measure by the proposed method applied on Oxford landmarks is 0.223 and this criterion for Corel dataset is 0.269. The obtained results illustrate that the proposed method improves the efficiency, speeds up recovery and reduces the storage space.

Object retrieval; Speeded Up Robust Features (SURF); Large-scale; Supervised; Training-Free