Performance Evaluation of SIFT-Based Descriptors for Object Recognition

Object recognition has become one of the most active research topics in computer vision in recent years. The set of features extracted from the training image is critical for good object recognition performance. The Scale Invariant Feature Transform (SIFT) was proposed by David Lowe in 1999; the SIFT features are local and effective for object recognition. In this paper we conducted a survey of recent related work on the SIFT descriptor, analyzed the evaluation criteria for object recognition, and analyzed the performance of the SIFT descriptor and extended SIFT descriptors based on common properties and evaluation criterion. The paper documents improvement strategies and trends of the SIFT descriptor and proposed extensions.

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