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Title

TRAINING-FREE OBJECT MATCHING AND RETRIEVAL USING SPEEDED UP ROBUST FEATURES

Pages

 Start Page 141 | End Page 153

Keywords

SPEEDED UP ROBUST FEATURES (SURF) 

Abstract

 Traditionally, OBJECT RETRIEVAL methods require a set of images of a specific object for training. In this paper, we propose a new OBJECT RETRIEVAL method using a single query image, without training, for a global object. The query image could 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 an object model. The ability to match partially a fine transformed object images results 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 obtained results illustrate that the proposed method improves efficiency, speeds up recovery and reduces the storage space.

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