ELF derives keypoint locations via gradients on pre-trained CNN feature maps and reaches repeatability and matchability scores comparable to specialized detectors on HPatches, Webcam, and photo-tourism data.
Descriptor Matching with Convolutional Neural Networks: a Comparison to SIFT
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abstract
Latest results indicate that features learned via convolutional neural networks outperform previous descriptors on classification tasks by a large margin. It has been shown that these networks still work well when they are applied to datasets or recognition tasks different from those they were trained on. However, descriptors like SIFT are not only used in recognition but also for many correspondence problems that rely on descriptor matching. In this paper we compare features from various layers of convolutional neural nets to standard SIFT descriptors. We consider a network that was trained on ImageNet and another one that was trained without supervision. Surprisingly, convolutional neural networks clearly outperform SIFT on descriptor matching. This paper has been merged with arXiv:1406.6909
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cs.CV 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
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ELF: Embedded Localisation of Features in pre-trained CNN
ELF derives keypoint locations via gradients on pre-trained CNN feature maps and reaches repeatability and matchability scores comparable to specialized detectors on HPatches, Webcam, and photo-tourism data.