Deep Local Descriptors with Domain Adaptation
| Autor: | Shuwen Qiu, Weihong Deng |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Domain adaptation
Training set business.industry Computer science Cognitive neuroscience of visual object recognition Pattern recognition 02 engineering and technology 010501 environmental sciences 01 natural sciences Domain (software engineering) 0202 electrical engineering electronic engineering information engineering Feature (machine learning) Maximum mean discrepancy 020201 artificial intelligence & image processing Artificial intelligence Layer (object-oriented design) Focus (optics) business 0105 earth and related environmental sciences |
| Zdroj: | Pattern Recognition and Computer Vision ISBN: 9783030033347 PRCV (2) |
| DOI: | 10.1007/978-3-030-03335-4_30 |
| Popis: | Due to the different distributions of training and testing datasets, the performance of the trained model based on the training set can rarely achieve the most optimal. Inspired by the successful application of domain adaptation in the object recognition area, we apply domain adaptation methods to CNN based local feature descriptors based on their own traits. Different from previous domain adaptation methods that focus only on the fully connected layer, we apply maximum mean discrepancy (MMD) criterion to both the fully connected layer and the convolutional layer, which makes the primary local filters of CNN adaptive to the target dataset in an unsupervised manner. Extensive experiments on Photo Tour and HPatches dataset show that domain adaption is effective to local feature descriptors, and, more importantly, the convolutional layer adaption can further improve the performance of traditional domain adaptation. |
| Databáze: | OpenAIRE |
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