Serge Belongie's top 10 favorite papers

转载：http://www.computer.org/portal/web/tpami/belongie

1. Principal Warps: Thin-Plate Splines and the Decomposition of Deformations by F.L. Bookstein

Citation: F.L. Bookstein, "Principal Warps: Thin-Plate Splines and the Decomposition of Deformations," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, no. 6, pp. 567-585, June 1989, doi:10.1109/34.24792

I'd like to say I learned about Thin-Plate Splines straight from the papers by Duchon or Meinguet, but I didn't. In fact, I found out about them from this excellent paper by Fred Bookstein. I remember very well punching in the coefficients of the numerical example in that paper into Matlab and realizing how helpful this approach would be to my work on shape matching.

2. The Design and Use of Steerable Filters by W.T. Freeman and E.H. Adelson

Citation: W.T. Freeman, E.H. Adelson, "The Design and Use of Steerable Filters," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 13, no. 9, pp. 891-906, Sept. 1991, doi:10.1109/34.93808

This is the first TPAMI paper I ever read, and it is also the reason I chose to make computer vision my career. I was hooked from their very first example of steered first derivatives of Gaussians. I subsequently devoted several years of my life to studying low level feature extraction, including a pilgrimage to the Mecca of image filtering in Linköping, Sweden.

3. In Defense of the Eight-Point Algorithm by R.I. Hartley

Citation: Richard I. Hartley, "In Defense of the Eight-Point Algorithm," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 6, pp. 580-593, June 1997, doi:10.1109/34.601246

"It's the normalization, stupid."

4. Normalized Cuts and Image Segmentation by Jianbo Shi and Jitendra Malik

Citation: Jianbo Shi, Jitendra Malik, "Normalized Cuts and Image Segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 888-905, Aug. 2000, doi:10.1109/34.868688

This was one of the first TPAMI papers whose formation I witnessed from start to finish, since Jianbo was my officemate. We all knew they had a hit on their hands with this one. We also knew that with the publication of this paper, our honeymoon phase with spectral clustering was over, and the nitty gritty phase was about to begin.

5. Compact Representations of Videos Through Dominant and Multiple Motion Estimation by Harpreet S. Sawhney and Serge Ayer

Citation: Harpreet S. Sawhney, Serge Ayer, "Compact Representations of Videos Through Dominant and Multiple Motion Estimation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 8, pp. 814-830, August, 1996, doi:10.1109/34.531801

Who could forget this paper's dynamic mosaics made from footage of Arnold riding a Harley in Terminator 2. The things they were doing with optical flow at Sarnoff Research Center in the mid-90s were indistinguishable from magic.

6. A Computational Approach to Edge Detection by John Canny

Citation: John Canny, "A Computational Approach to Edge Detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 8, no. 6, pp. 679-698, Nov. 1986, doi:10.1109/TPAMI.1986.4767851

Before the SVD mania of the 90s, and long before the boosting craze of the 00s, a handful of towering contributions in the areas of edge detection, optical flow and regularization theory were developed on the foundations of variational calculus. The Canny Edge Detector, developed in the early 80s, was one such contribution. 25 years later it is required learning in virtually every beginning course in computer vision. Not bad for a Master's Thesis!

7. Fast Approximate Energy Minimization via Graph Cuts by Yuri Boykov, Olga Veksler, and Ramin Zabih

Citation: Yuri Boykov, Olga Veksler, Ramin Zabih, "Fast Approximate Energy Minimization via Graph Cuts,"IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 11, pp. 1222-1239, November, 2001, doi:10.1109/34.969114

For a few years there it seemed that every problem I was working on could be written down with a cost function that Yuri, Olga and Ramin's code could solve for me quickly and accurately.

8. Joint Induction of Shape Features and Tree Classifiers by Yali Amit, Donald Geman, and Kenneth Wilder

Citation: Yali Amit, Donald Geman, Kenneth Wilder, "Joint Induction of Shape Features and Tree Classifiers,"IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 11, pp. 1300-1305, Nov. 1997, doi:10.1109/34.632990

Quantized tags, approximate geometric arrangements and randomized trees. There were no SIFT or HoG features back then, and the binary handwritten digits were a far cry from the sheep and motorbikes of PASCAL and MSRC, but the essential constellation based recognition approach proposed by this paper was brilliant and ahead of its time.

9. Learning to Detect Objects in Images via a Sparse, Part-Based Representation by Shivani Agarwal, Aatif Awan, and Dan Roth

Citation: Shivani Agarwal, Aatif Awan, Dan Roth, "Learning to Detect Objects in Images via a Sparse, Part-Based Representation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 11, pp. 1475-1490, Nov. 2004, doi:10.1109/TPAMI.2004.108

We still don't know what a "part" is, but that philosophical sticking point didn't stop this paper from making a big impact in object category detection.

10. An Eigendecomposition Approach to Weighted Graph Matching Problems by S. Umeyama

Citation: S. Umeyama, "An Eigendecomposition Approach to Weighted Graph Matching Problems," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 10, no. 5, pp. 695-703, Sept. 1988, doi:10.1109/34.6778M

Spectral graph matching is the lesser known sibling of spectral clustering, but it is nonetheless filled with interesting theoretical nuggets, many of which I encountered for the first time in this paper. I fondly remember this as the paper that prompted me to check out a copy of Papadimitriou and Stieglitz to find out about this so called "Hungarian Algorithm."