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DELF: DEep Local Features

This project presents code for extracting DELF features, which were introduced with the paper "Large-Scale Image Retrieval with Attentive Deep Local Features". A simple application is also illustrated, where two images containing the same landmark can be matched to each other, to obtain local image correspondences.

DELF is particularly useful for large-scale instance-level image recognition. It detects and describes semantic local features which can be geometrically verified between images showing the same object instance. The pre-trained model released here has been optimized for landmark recognition, so expect it to work well in this area. We also provide tensorflow code for building the DELF model, which could then be used to train models for other types of objects.

If you make use of this code, please consider citing:

"Large-Scale Image Retrieval with Attentive Deep Local Features",
Hyeonwoo Noh, Andre Araujo, Jack Sim, Tobias Weyand, Bohyung Han,
Proc. ICCV'17

Installation

To be able to use this code, please follow these instructions to properly install the DELF library.

Quick start: DELF extraction and matching

Please follow these instructions. At the end, you should obtain a nice figure showing local feature matches, as:

MatchedImagesExample

Code overview

DELF's code is located under the delf directory. There are two directories therein, protos and python.

delf/protos

This directory contains three protobufs:

  • datum.proto: general-purpose protobuf for serializing float tensors.
  • feature.proto: protobuf for serializing DELF features.
  • delf_config.proto: protobuf for configuring DELF extraction.

delf/python

This directory contains files for several different purposes:

  • datum_io.py, feature_io.py are helper files for reading and writing tensors and features.
  • delf_v1.py contains the code to create DELF models.
  • feature_extractor.py contains the code to extract features using DELF. This is particularly useful for extracting features over multiple scales, with keypoint selection based on attention scores, and PCA/whitening post-processing.

Besides these, other files in this directory contain tests for different modules.

The subdirectory delf/python/examples contains sample scripts to run DELF feature extraction and matching:

  • extract_features.py enables DELF extraction from a list of images.
  • match_images.py supports image matching using DELF features extracted using extract_features.py.
  • delf_config_example.pbtxt shows an example instantiation of the DelfConfig proto, used for DELF feature extraction.

Dataset

The Google-Landmarks dataset will be released together with a Kaggle-hosted landmark recognition competition. We will include the link to it here once it is launched (expect this to be done around mid-January, 2018).

Maintainers

André Araujo (@andrefaraujo)

Release history

October 26, 2017

Initial release containing DELF-v1 code, including feature extraction and matching examples.

Thanks to contributors: André Araujo, Hyeonwoo Noh, Youlong Cheng, Jack Sim.