It seems as though this approach could suffer from early thresholding in generating its set of Tangent segments T, since the algorithm determines what to include in this set prior to trying to connect it with its neighbors.

Also, I was just curious what 'Markov assumptions that do not capture global structure' many other boundary detection algorithms used (mentioned in the introduction).

Edited by author 10-24-2006 12:39 AM
I like the paper's contribution of a way to integrate information at multiple scales. However, while it is a good idea, whether or not the framework is of any use depends on solving extremely difficult problems. These problems are mentioned in the discussion section and include defining the correct goodness metric for contours and developing a shape model of natural scenes. This difficulty is highlighted by the fact that after so much fanfare about using information at higher resolution the authors downsample the training images in order to improve results. Just an observation, not much of a question here.

Though the authors write "In
this paper we are interested in computing the boundaries of
a broad range of salient objects", I wonder how this method would perform if it was trained on one specific class of objects like human skin, etc.

Edited by author 10-24-2006 08:08 AM
To Boris:
For other class of images, depending on
(a) the underlying boundary detector in [15] which can be retrained.
(b) if multiscale(MS) is beneficial.

Some my points.
(a) Why restrict the application to only natural images? (due to the MS property?)

(b) Taking top 20 candidate contours in testing hides the high FP rate problem. In data mining view, the author cheated by looking at testing data!.

(c) Fig.4 doesn't agree with Fig.5. MS=0.5(too high) while EJ=0.73(too low) and SS>EJ, huh? The examples selected in Fig.4 is extremely biased to demo the MS algorithm.

(d) The grouping algorithm which always keep top K candidate after branch has a fancy name in NLP called "beam search".

(e) What's the major benefit of using Fourier Transform, does it really save computation time? Can you briefly describe the time that FFT used.