Figure 1. Comparisons of descriptors in terms of both storage requirement and matchiing performance.

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Abstract

This paper proposes a novel local descriptor through accumulated stability voting (ASV). The stability of feature dimensions is measured by their differences across scales. To be more robust to noise, stability is further quantized by thresholding. The principle of maximum entropy is utilized for determining the best thresholds for optimizing discriminant power of the resultant descriptor. Accumulating stability renders a real-valued descriptor and it can be converted into a binary descriptor by an additional thresholding. The real-valued descriptor attains high matching accuracy while the binary descriptor makes a good compromise between storage and accuracy. Our descriptors are simple yet effective, and also very easy to implement. In addition, our descriptors require no training. Experiments on popular benchmark demonstrate the effectiveness of our descriptors and superiority to the state-of-the-art descriptors.

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ASV framework

Figure 2. Two-stage thresholding.

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Figure 3. Head-to-head comparisons.

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Paper

https://drive.google.com/open?id=0B_q2Q4O-rzP6bmlxTzFyMGJfaWs

Poster

https://drive.google.com/file/d/0B_q2Q4O-rzP6aTVFTGJqRkVJZVU/view?usp=sharing

Code

Github
https://github.com/shamangary/ASV
(I suggest you run the Oxford dataset first since the number of the image pairs are 40 while Fischer dataset contains 400 pairs.)

Folder arrangement:
unzipped folder----------Oxford
         |
         ------Fischer
         |
         ------vlfeat-0.9.18
         |
         ------README.md
         |
         ------LICENSE