Computer Vision and Image Understanding
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
Object recognition based on the region of interest and optical bag of words model
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
Reading the legends of Roman Republican coins
Journal on Computing and Cultural Heritage (JOCCH)
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Visual content description is a key issue for the task of machine-based visual object categorization (VOC). A good visual descriptor should be both discriminative enough and computationally efficient while possessing some properties of robustness to viewpoint changes and lighting condition variations. The recent literature has featured local image descriptors, e.g. SIFT, as the main trend in VOC. However, it is well known that SIFT is computationally expensive, especially when the number of objects/concepts and learning data increase significantly. In this paper, we investigate the DAISY, which is a new fast local descriptor introduced for wide baseline matching problem, in the context of VOC. We carefully evaluate and compare the DAISY descriptor with SIFT both in terms of recognition accuracy and computation complexity on two standard image benchmarks - Caltech 101 and PASCAL VOC 2007. The experimental results show that DAISY outperforms the state-of-the-art SIFT while using shorter descriptor length and operating 3 times faster. When displaying a similar recognition accuracy to SIFT, DAISY can operate 12 times faster.