Similar

I have a philosophical question and wait for your answer.

How do you define the Term "Similar"?

When 2 images are considered as “Similar images”?

 

 

 

According to Google, as similar is defined:

• marked by correspondence or resemblance; "similar food at similar prices"; "problems similar to mine"; "they wore similar coats"
• alike(p): having the same or similar characteristics; "all politicians are alike"; "they looked utterly alike"; "friends are generally alike in background and taste"
• like: resembling or similar; having the same or some of the same characteristics; often used in combination; "suits of like design"; "a limited circle of like minds"; "members of the cat family have like dispositions"; "as like as two peas in a pod"; "doglike devotion"; "a dreamlike quality"
• (of words) expressing closely related meanings
• exchangeable: capable of replacing or changing places with something else; permitting mutual substitution without loss of function or suitability; "interchangeable electric outlets" "interchangeable parts"
  wordnetweb.princeton.edu/perl/webwn
• In linear algebra, two n-by-n matrices A and B are called similar if
  en.wikipedia.org/wiki/Similar_(linear_algebra)
• Having traits or characteristics in common; alike, comparable; of triangles, etc., having corresponding angles equal and corresponding line ...
  en.wiktionary.org/wiki/Similar
• similarly - in like or similar manner; "He was similarly affected"; "some people have little power to do good, and have likewise little strength to resist ...
  wordnetweb.princeton.edu/perl/webwn
• similarly - in a like style or manner; Used to link similar items
  en.wiktionary.org/wiki/similarly
• Figures that have the same shape but not necessarily the same size. Similar polygons have corresponding angles congruent and corresponding sides in proportion. Congruent is a special case of similar where the ratio of the corresponding sides is 1-1.
  www.aug.edu/~lcrawford/Web_3242/GLOSSARY.htm
• Denotes meaning similarity between words that cannot always be used instead of each other, for instance because they only share a part of their ...
  www.hyperdic.net/en/doc/word
• Selects like-color pixels throughout the image.
  www.uwec.edu/Help/PhotoshopCS3/terms.htm
• When data are described as "similar" this indicates the difference is likely due to chance as opposed to a real difference between two populations, even if the data points are different. ...
  www.clark.wa.gov/public-health/reports/definitions.html
• Two objects such that the distance between any two points of one object is a particular constant times the distance between the corresponding ...
  quizlet.com/769330/more-geometry-terms-flash-cards/
• similarly - 2 ) has been synthesized in NSPs of at least two different shapes and crystalline structures, each of which may have different toxicities. ...
  www.azonano.com/Details.asp

Send me your opinion at savvash<at>gmail<dot>com and i will post it here.

Top Ten Articles -TPAMI

This year, TPAMI is celebrating its 30th anniversary. To mark this milestone, the IEEE Computer Society’s Publishing Services Department asked journal volunteers to submit their All-Time Favorite Top 10 list and explain their reasons for choosing the papers. Free, limited-time access is available to all of the papers on the list.

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."

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

VDS(θ)

My collaborators (Vicky and Dim) are working on new video summarization project based on multimodal data and fuzzy classifiers. The proposed technique automatically generates summaries from on-line videos (YouTube). Each frame may participate in one or more than one of the generated classes. The application, once more, will be open source.

Here is a screenshot. More details as well as the paper will be added soon.

The 10th IEEE International Conference on Signal Processing

Beijing, China

October 24-28, 2010

http://icsp10.bjtu.edu.cn

Important Deadline:

Submission of Papers: June 15, 2010

The International Conference on Signal Processing (ICSP), sponsored by the IEEE Beijing Section, is the premier forum for the presentation of technological advances and research results in the fields of theoretical, experimental, and applied signal processing. ICSP 2010 will bring together leading engineers and scientists in signal processing from around the world. Research frontiers in fields ranging from traditional signal processing applications to evolving multimedia and video technologies are regularly advanced by results first reported in ICSP technical sessions.

Topics include, but are not limited to:

A. Digital Signal Processing (DSP)

B. Spectrum Estimation & Modeling

C. TF Spectrum Analysis & Wavelet

D. Higher Order Spectral Analysis

E. Adaptive Filtering &SP

F. Array Signal Processing

G. Hardware Implementation for Signal Processing H. Speech and Audio Coding I. Speech Synthesis & Recognition J. Image Processing & Understanding K. PDE for Image Processing L. Video compression &Streaming M. Computer Vision & VR N. Multimedia & Human-computer Interaction O. Statistic Learning & Pattern Recognition P. AI & Neural Networks Q. Communication Signal processing R. SP for Internet and Wireless Communications S. Biometrics & Authentification T. SP for Bio-medical & Cognitive Science U. SP for Bio-informatics V. Signal Processing for Security W. Radar Signal Processing X. Sonar Signal Processing and Localization Y. SP for Sensor Networks Z. Application & Others

*Attention*

Under the support of numerous reviewers and authors, ICSP has been holded for 20 years. In this session, as a celebration for ICSP, we will hold celebration events and awards, which include Outstanding Paper Award, Outstanding Student Paper Award, etc. For details, please visit http://icsp10.bjtu.edu.cn .

*Proceedings*

The proceedings with Catalog number of IEEE and Library of Congress will be published prior to the conference in both hardcopy and CD-ROM, and distributed to all registered participants at the conference. The proceedings will be indexed by EI.

*Paper Submission*

Prospective authors are invited to submit full-length, four-page, double-column papers, including figures and references, to the ICSP Technical Committee by June 15, 2010 at http://icsp10.bjtu.edu.cn. For questions about paper submission, please contact the technical program secretaries, Ms. TANG Xiaofang and Dr. AN Gaoyun at bfxxstxf@bjtu.edu.cn and gyan@bjtu.edu.cn .

For more information, please visit the ICSP 2010 web site at:

http://icsp10.bjtu.edu.cn.

Software for Unwrapping Omnidirectional Images

A small application for unwrapping omnidirectional images using polar to Cartesian coordinate conversion. The size and aspect ratio of the produced images can be adjusted and the application performs bilinear or bicubic interpolation in order to improve the quality. The center of the omnidirectional image can be detected automatically using either a very simple and fast algorithm based on image thresholding or a slower but much more robust method based on edge detection and Hough transform. An example of using the second method is shown below.

Videos presenting sequences of unwrapped omnidirectional images taken from the COLD database can be downloaded here, here and here.
Download, installation and usage instructions for both Linux and Windows can be found below. If you have any questions, you experience problems with the software or you have spotted a bug, please contact Andrzej Pronobis.

Download and Installation

The application is known to compile in both Linux and Windows and depends on the OpenCV library. The source code can be downloaded either as a tar.gz file (for Linux users) or zip file (for Windows users):
   • Tar/gzip file (443.46 kB)
   • Zip file (446.30 kB)
Binaries for both operating systems are also available:
   • Linux binary (449.10 kB)
   • Windows binary (584.34 kB)
CMake is used as a build system for the sources. Windows users can install MinGW to get a C++ compiler. To build from the sources, use either the 'build.sh' or 'build.bat' script.

Examples:

• Estimating the center of the image using the thresholding-based method. 5 pixel boundary on top and bottom of the input image is excluded:
  unwrap -tc 30 -eb 0 5 0 5 -fi filtered.jpg 40 230 input.jpg
• Estimating the center of the image using the edge-based method. Two "debug" images are created:
  unwrap -ec 230 240 330 240 20 20 -fi filtered.jpg -ti test.jpg 40 230 input.jpg
• Unwrapping using fixed center and no interpolation or scaling:
  unwrap -uw output.jpg -fc 330 240 40 230 input.jpg
• Unwrapping using automatic center point detection and bicubic interpolation:
  unwrap -uw output.jpg -ec 230 240 330 240 20 20 -bc 40 230 input.jpg
• As above, but with vertical resolution of the output increased 2x:
  unwrap -uw out.jpg -ec 230 240 330 240 20 20 -bc -sy 2 40 230 in.jpg

http://www.csc.kth.se/~pronobis/software/unwrap/

New Version of img(Rummager)

Change log
-------------------------------------
2009-05-05 1.0.3776
img Retrieval
1.New Shape Descriptor (CSPD)
2.SpCD bug fixed

img Retrieval
3.MPEG-7 Descriptors Fusion
    -Using HIS*
        -Download Empirical (Historical) Files From the WEB.
    -Using Z-Score
    -Using Borda Count
    -Using IRP
    -Using Linear Sum
4.MPEG-7 and CCD Descriptors Fusion
    -Using HIS*
        -Download Empirical (Historical) Files From the WEB.
    -Using Z-Score
    -Using Borda Count
    -Using IRP
    -Using Linear Sum
5.From now on we are using a Compact Version of the BTDH for indexing and retrieval
6.New descriptor (B-CEDD). During the search process, an image query is entered and the system returns images with a similar content. Initially, the similarity/distance between the query and each image in the database is calculated with the B-CEDD descriptor, and only if the distance is smaller than a predefined threshold,the comparison of their CEDDs is performed.
7.Now you can save the retrieval results in trec_eval format
8.Indexing is now working with *.bmp,*.jpg and *.png

img(Rummager)