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Local Convolutional Features with Unsupervised Training for Image Retrieval
Abstract : Patch-level descriptors underlie several important computer vision tasks, such as stereo-matching or content-based image retrieval. We introduce a deep convolutional architecture that yields patch-level descriptors, as an alternative to the popular SIFT descriptor for image retrieval. The proposed family of descriptors, called Patch-CKN, adapt the recently introduced Convolutional Kernel Network (CKN), an unsupervised framework to learn convolutional architectures. We present a comparison framework to benchmark current deep convolutional approaches along with Patch-CKN for both patch and image retrieval, including our novel ``RomePatches'' dataset. Patch-CKN descriptors yield competitive results compared to supervised CNNs alternatives on patch and image retrieval.
Cited literature [42 references]
https://hal.inria.fr/hal-01207966
Contributor : Thoth Team <>
Submitted on : Thursday, October 1, 2015 - 4:27:12 PM
Last modification on : Thursday, March 26, 2020 - 8:49:29 PM
Document(s) archivé(s) le : Saturday, January 2, 2016 - 11:20:59 AM
Contributor : Thoth Team <>
Submitted on : Thursday, October 1, 2015 - 4:27:12 PM
Last modification on : Thursday, March 26, 2020 - 8:49:29 PM
Document(s) archivé(s) le : Saturday, January 2, 2016 - 11:20:59 AM
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deep_patches.pdf
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