Skip to content

Latest commit

 

History

History
146 lines (104 loc) · 5.76 KB

README.md

File metadata and controls

146 lines (104 loc) · 5.76 KB
Raw

Vector of Locally Aggregated Descriptors (VLAD)

Description

This repository is an implementation of VLAD, which was originally formulated by Hervé Jégou in [1]. The implementation is part of my bachelors-thesis termed "Computer Vision and Machine Learning for marker-free product identification".

VLAD is an algorithm that allows a user to aggregate local descriptors into a compact, global representation. It's derived from the Bag of Features approach [2] and related to the Fisher vector [3].

This repository is a WIP and should not be considered production-ready. I took a bit of inspiration from Jorjassos implementation and tried to make it better. Improved versions of the original formulation are also implemented. I took them from [1, 4, 5] and inserted references in the code.

Dependencies

  • Numpy
  • Scikit-Learn
  • Progressbar2
  • OpenCV (for the examples)

Install-Instructions

TODO

Usage

The API is based on the wonderful Scikit-Learn API, which uses the basic notion of fit/ predict/ transform.

To include VLAD in the current file just write:

from vlad import VLAD

On initialization, the number of visual words (k) and the norming-scheme are given. Norming is a crucial difference in between different implementations [1, 4, 5] with [5] containing the preferable one. To instantiate a VLAD-object write:

vlad = VLAD(k=16, norming="RN")  # Defaults are k=256 and norming="original"

After having instantiated the object you can fit the visual vocabulary with:

vlad.fit(X)

The fit-function also returns the instance (again, sklearn-style), so the following two are equivalent:

vlad = VLAD(k=16, norming="RN")
vlad.fit(X)
# ...
vlad = VLAD(k=16, norming="RN").fit(X)

X is a tensor of image-descriptors (m x d x n), where m is the number of descriptors per image, d is the number of dimensions per descriptor and n is the total number of image-descriptors. It's best to use image-descriptors in euclidean space (Such as SIFT or RootSIFT [6]), rather than in hamming space, as the KMeans-clustering won't work properly with hamming-descriptors.

Whenever a visual dictionary is fitted, the dictionary is saved to disc and can be loaded manually to bypass training.

To check for an image one can write:

vlad.predict(imdesc)  # imdesc is a (m x d) descriptor-matrix

to get the image-index with maximum similarity. Alternatively

vlad.predict_proba(imdesc)

can be used to obtain a Numpy-array with all similarity scores.

If you want to work with the VLAD-descriptors outside of the class, the transform- and fit_transform-functions can be utilized:

vlads = vlad.transform(descriptor_tensor)  # Call on fitted model

vlads = vlad.fit_transform(descriptor_tensor)  # Can be called on a non-fitted model

Documentation

Documentation can be found at [...] TODO

Roadmap

Task Status
Original formulation (SSR, L2) [1] Done
Use RootSIFT-descriptors [6] Done
Try with more descriptors TODO
Try with dense descriptors TODO
Intra-Normalization [4] Done
Residual-Normalization (RN) [5] Done
Local Coordinate System (LCS) [5] Done
Dimensionality-Reduction [7,8] TODO
Quantization [9] Done
Generalization using multiple vocabularies [7] TODO
Make documentation TODO
Include Tests TODO
Include Install-Instructions TODO
Include Usage-Examples Done
Provide example notebooks Done

References

[1]: Jégou, H., Douze, M., Schmid, C., & Pérez, P. (2010, June). Aggregating local descriptors into a compact image representation. In 2010 IEEE computer society conference on computer vision and pattern recognition (pp. 3304-3311). IEEE.

[2]: Sivic, J., & Zisserman, A. (2003, October). Video Google: A text retrieval approach to object matching in videos. In null (p. 1470). IEEE.

[3]: Perronnin, F., Sánchez, J., & Mensink, T. (2010, September). Improving the fisher kernel for large-scale image classification. In European conference on computer vision (pp. 143-156). Springer, Berlin, Heidelberg.

[4]: Arandjelovic, R., & Zisserman, A. (2013). All about VLAD. In Proceedings of theIEEE conference on Computer Vision and Pattern Recognition (pp. 1578-1585).

[5]: Delhumeau, J., Gosselin, P. H., Jégou, H., & Pérez, P. (2013, October).Revisiting the VLAD image representation. In Proceedings of the 21st ACM international conference on Multimedia (pp. 653-656).

[6]: Arandjelović, R., & Zisserman, A. (2012, June). Three things everyone should know to improve object retrieval. In 2012 IEEE Conference on Computer Vision and Pattern Recognition (pp. 2911-2918). IEEE.

[7]: Jégou, H., & Chum, O. (2012, October). Negative evidences and co-occurences in image retrieval: The benefit of PCA and whitening. In European conference on computer vision (pp. 774-787). Springer, Berlin, Heidelberg.

[8]: Jegou, H., Perronnin, F., Douze, M., Sánchez, J., Perez, P., & Schmid, C. (2011). Aggregating local image descriptors into compact codes. IEEE transactions on pattern analysis and machine intelligence, 34(9), 1704-1716.

[9]: Jegou, H., Douze, M., & Schmid, C. (2010). Product quantization for nearest neighbor search. IEEE transactions on pattern analysis and machine intelligence, 33(1), 117-128.