Efficient discriminative local learning for object recognition

Lin, Yen‐Yu; Tsai, Jyun-Fan; Liu, Tyng-Luh

doi:10.1109/iccv.2009.5459182

Cited by 6 publications

(1 citation statement)

References 35 publications

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“…Our classification rate achieves 66.4 ± 1.0 [%]. This performance is lower than the state-of-the-art methods (Lin et al [28] (75.8 ± 1.1 [%]), Boiman et al [15] (72.8 ±0.39 [%]), Bosch et al [7] (70.4 ± 0.7 [%])), but has comparable results with Frome et al [29] (63.2 [%]) , Zhang et al [30] (59.1 ± 0.56 [%]), and Lazebnik et al [21] (56.4 [%]). It should be noted that our classification method is very simple and does not use the optimization of weight for each feature, and the learning and classification times are about 150 [sec] and 10 [msec/frame].…”

Section: Object Recognitionmentioning

confidence: 73%

Improving Local Descriptors by Embedding Global and Local Spatial Information

Harada

Nakayama

Kuniyoshi

2010

Computer Vision – ECCV 2010

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In this paper, we present a novel problem: "Given local descriptors, how can we incorporate both local and global spatial information into the descriptors, and obtain compact and discriminative features?" To address this problem, we proposed a general framework to improve any local descriptors by embedding both local and global spatial information. In addition, we proposed a simple and powerful combination method for different types of features. We evaluated the proposed method for the most standard scene and object recognition dataset, and confirm the effectiveness of the proposed method from the viewpoint of speed and accuracy.

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