A summary of Distinctive Image Features from Scale-Invariant Keypoints

David G. Lowe International Journal of Computer Vision, 2004 DOI

For the summary of the paper, go to the Summary section of this article.

Table of Contents

• A summary of Distinctive Image Features from Scale-Invariant Keypoints
  • Table of Contents
  • First Pass
    • Problem
    • Motivation
    • Category
    • Context
    • Contributions
  • Second Pass
    • Background Work
    • Figures, Diagrams, Illustrations, and Graphs
    • Clarity
    • Relevant Work
    • Methodology
    • Author Assumptions
      • Correctness
    • Future Directions
    • Open Questions
    • Author Feedback
  • Summary
  • Summarization Technique
  • Citations

First Pass

Read the title, abstract, introduction, section and sub-section headings, and conclusion

Problem

What is the problem addressed in the paper?

This paper aims to address image matching challenges by finding scale invariant features from images. These features perform well against images that are subject to blurring and noise.

Motivation

Why should we care about this paper?

We should care about this paper as it presents an efficient method for generating image features that are invariant to scale and rotation changes. This allows for images to be taken in arbitrary locations and at different locations to then be matched with similar objects in a different image. In other words, it presents an efficient way of generating image features that can be used to compare how similar two images are regardless of scale and rotation differences.

Category

What type of paper is this work?

This is an algorithms paper focused on image matching and retrieval.

Context

What other types of papers is the work related to?

This work is most similar to work discussing image feature extraction and image matching and retrieval techniques.

Contributions

What are the author’s main contributions?

Their main contributions were a methodology for extracting scale invariant image features as well as methods for comparing features between images for the purposes of image matching and retrieval.

Second Pass

A proper read through of the paper is required to answer this

Background Work

What has been done prior to this paper?

Work has been done to develop image feature extractors. These extractors were initially used for stereo and short range motion tracking. However, they are now capable of more complex tasks. These include image recognition and retrieval. All of these feature extractors produce a representation of the image that can be used to compare one image against another.

Figures, Diagrams, Illustrations, and Graphs

Are the axes properly labeled? Are results shown with error bars, so that conclusions are statistically significant?

All of the figures are clearly labeled. I do find the lines on the line charts are a bit difficult to distinguish due to the usage of a tight dashed line. But that’s on me, not the paper.

Clarity

Is the paper well written?

The paper is well written, if a bit dense. There is an argument to be made that this paper is two papers in one. One about a novel feature extraction technique, and a second about image retrieval with the usage of feature extractors.

Relevant Work

Mark relevant work for review

The following relevant work can be found in the Citations section of this article.

2. Brown, M. and Lowe, D.G. 2002. Invariant features from interest point groups. In British Machine Vision Conference, Cardiff, Wales, pp. 656–665.
3. Carneiro, G. and Jepson, A.D. 2002. Phase-based local features. In European Conference on Computer Vision (ECCV), Copenhagen, Denmark, pp. 282–296.
4. Crowley,J.L.and Parker,A.C.1984.A representation for shapes based on peaks and ridges in the difference of low-pass transform. IEEE Trans. on Pattern Analysis and Machine Intelligence, 6(2):156–170.
5. Fergus, R., Perona, P., and Zisserman, A. 2003. Object class recognition by unsupervised scale-invariant learning. In IEEE Conference on Computer Vision and Pattern Recognition, Madison, Wisconsin, pp. 264–271.
6. Harris,C.and Stephens,M.1988.A combined corner and edge detector. In Fourth Alvey Vision Conference, Manchester, UK, pp. 147–151.
7. Koenderink, J.J. 1984. The structure of images. Biological Cybernetics, 50:363–396.

Methodology

What methodology did the author’s use to validate their contributions?

For their feature extractor, they created a dataset of images and their features, then performed transformations on images present in the dataset to generate new features. With these variables, they were able to measure the performance of their feature extractor and how well it was able to identify invariant features. With respect to their testing on object recognition and image retrieval, they utilized K Nearest Neighbor (KNN) algorithms to accomplish this.

Author Assumptions

What assumptions does the author(s) make? Are they justified assumptions?

This work doesn’t rely on the usage of Deep Neural Networks (DNNs) to learn the representation of images. Because of this, their work relies on hand crafted filters and algorithms to extract features. This could result in algorithmic bias or generate results that are susceptible to the views of the author.

Correctness

Do the assumptions seem valid?

Keeping in mind that this paper was published in 2004, this assumption seems valid for the time. Due to the AI winter as well as the limited usage of GPUs for the purposes of training DNNs, handcrafting feature extractors was a valid usage.

Future Directions

My own proposed future directions for the work

While I can’t say that image retrieval is of much interest to me, I would like to explore how to perform object detection or image recognition using this feature extractor. Additionally, it would be really cool to see if I could utilize this feature extractor on low powered devices for the purposes of image classification.

Open Questions

What open questions do I have about the work?

The Background section of this paper mentioned the usage of feature detectors for motion capture. Is that possible with this feature extractor? What does that space look like today vs 2004 vs 1990s? What would happen if I trained a Deep Learning model on SIFT features? Could I get a comparable output to a CNN with respect to image classification (for example)?

Author Feedback

What feedback would I give to the authors?

This is a great paper that introduces a novel technique for performing feature extraction. However, it is a bit dense and could’ve been split into two separate papers. One being an algorithms paper presenting the feature extractor, and another being a case study of feature extractor performance on many tasks.

Summary

A summary of the paper

The paper Distinctive Image Features from Scale-Invariant Keypoints by David G. Lowe [1] presents a novel image feature extractor called Scale Invariant Feature Transform (SIFT). SIFT is an algorithm to extract features from an image that are invariant (do not change) to scale and rotation. These features can be used to perform image retrieval and object recognition by utilizing nearest neighbor algorithms such as KNN or ANN.

This summary was kept short as I have been sitting on this summary for well over two weeks now with no progress and just want to get something out. Sorry for the brevity and weak summary.

Summarization Technique

This paper was summarized using a modified technique proposed by S. Keshav in his work How to Read a Paper [0].

Citations