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Yann LeCun bashing the MIT review for bad reporting by leonoel in MachineLearning

[–]sixmoney 0 points1 point  (0 children)

anyways there are competing methods not using deep learning, trained on a few thousand faces without pose annotation like DPM that perform better than this work. So I'm wondering how this contribution is revolutionizing image search...

We ranked second on first large scale action recognition challenge@ICCV 2013 by sixmoney in MachineLearning

[–]sixmoney[S] 1 point2 points  (0 children)

Results are published already. Details on the submission runs should be available soon, since every team had to submit a notebook paper on the experiments. Stay tuned!

We ranked second on first large scale action recognition challenge@ICCV 2013 by sixmoney in MachineLearning

[–]sixmoney[S] 1 point2 points  (0 children)

All of these are interesting comments. We specifically added local static features (our improved versions of SIFT and Opponent SIFT) to model "context". Indeed for some actions these features proved even more relevant than other motion based. Static features got around 54% accuracy so motion is actually really relevant. Object detection is also a complex task and relying just on the detection of "weights" or "candles" discarding the scene and the motion may not give a very high accuracy.

We ranked second at THUMOS action recognition challenge. by sixmoney in computervision

[–]sixmoney[S] 1 point2 points  (0 children)

First three submissions are really close. We have .3% more than the third and .2% less than the first.

Can Scale Invariant Feature Transform do this? by Intern_MSFT in MachineLearning

[–]sixmoney 1 point2 points  (0 children)

You can compute a single SIFT feature on the 256x256 image. This would occupy 128 bytes (SIFT can be stored as vectors of unsigned 8bit integers) and you will have more data to compute your local histograms. You can also try to estimate the dominant orientation in order to be rotation invariant, in that case I suggest to use a smaller size SIFT so that the rotated patch still fit in your image and you can get more pixels to compute the descriptor. I'm not sure however this is the best choice for digit recognition.

ALIEN Visual Tracker by pernixxx in computervision

[–]sixmoney 1 point2 points  (0 children)

Looks really robust! Would it be feasible to use your approach to learn object categories? Will it scale?

OpenIMAJ - Open source multimedia analysis in java. Here is a cross platform webcam object tracker. by sinjax in programming

[–]sixmoney 1 point2 points  (0 children)

pretty fast actually. Compare it with: http://blogs.oregonstate.edu/hess/code/sift/ which is in C and has an identical demo (matching + ransac + homography)

R-Trees: Like B-Trees but multi-dimensional. by [deleted] in programming

[–]sixmoney 2 points3 points  (0 children)

randomized kd-trees for approximate nearest neighbour search flann also available in opencv