Recognizing trees at a distance with discriminative deep feature learning

Zhen Zuo, G. Wang
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引用次数: 2

Abstract

We investigate discriminative features that are able to improve classification accuracy on visually similar classes. To this end, we build a deep feature learning network, which learns features with discriminative constraint in each single layer module, and learns multiple levels of features for hierarchical image representation. Specifically, the network encodes the discriminative information by automatically selecting the informative features, and forcing them to be closer to the features extracted from the same class than the features from different classes. We also collect a new fine-grained dataset containing 51 common tree species in Singapore. All the images are taken at a distance with large intra class variance, which makes the tree species hard to be distinguished. Our experimental results show that we are able to achieve 78.03% in accuracy on this challenging dataset, which is 8.48% higher than general hand-designed feature.
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基于判别深度特征学习的远距离树木识别
我们研究了能够在视觉上相似的类上提高分类精度的判别特征。为此,我们构建了一个深度特征学习网络,该网络在每个单层模块中学习带有判别约束的特征,并学习多层特征进行分层图像表示。具体来说,网络通过自动选择信息特征对判别信息进行编码,并迫使它们更接近从同一类中提取的特征,而不是从不同类中提取的特征。我们还收集了一个新的细粒度数据集,其中包含新加坡的51种常见树种。所有的图像都是在类内方差较大的距离上拍摄的,这使得树种难以区分。我们的实验结果表明,我们能够在这个具有挑战性的数据集上达到78.03%的准确率,比一般手工设计的特征高8.48%。
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