Feature extraction, feature binarization and image retrieval examples #141
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Inspired by @kencoken's commit f36e715 kencoken@f36e715 Related issues: #20: Extract the middle features #112: pythonic export of features and params for wrapper #139: About dump_network.cpp
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@kloudkl this looks awesome! Please PR this to the dev branch, and we will merge very soon. |
This was referenced Feb 25, 2014
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May 17, 2016
Deprecate master branch
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This pull request serves for two purposes.
First, CAFFE represents Convolution Architecture For Feature Extraction. So let's have a feature extraction example. To this end, Has/Get Blob/Layer methods are added to simplify feature extraction.
Second, the very natural next step is to apply the extracted features in practical applications, e.g. image retrieval. The image retrieval demo can also be deemed as a baseline method. Image retrieval is fastest when using binary features. But putting all the steps of a complete pipeline in an example is too complex. Thus a feature separate feature binarization example is split out.
Related issues:
#20: Extract the middle features
#112: pythonic export of features and params for wrapper
#139: About dump_network.cpp