CPSC 461 Information Structures III
|  | Copyright (C) 2003 Katrin Becker Last Modified June 2, 2003 04:22 PM
Educational Object Repositories Ideas for Your Assignment (Under Construction...)
http://www.sca.k12.nf.ca/staff/ccook/pd2003/frame.htm
http://eastcaryms.wcpss.net/OBJECT_%20REPOSITORIES.htm
http://www.netera.ca/downloads/neteranews/repnews_may2002.pdf
http://www.imsproject.org/otf/vancouver/presentations/active%20Content/gilbertPaquetteIMS-Vancouver.pdf
Assignment Ideas Links:
|  | Previous Image Matching Assignment
Reference Links:
DDJ: Image Authentication for a Slippery New Age : Knowing when images have been changed By Steve Walton |  |
Related Papers from: Proceedings of the eleventh international conference on Information and knowledge management 2002 , McLean, Virginia, USA Comment: Note the references - many of these papers also relate to problems we are trying to solve.
A compact and efficient image retrieval approach based on border/interior pixel classification Renato O. Stehling, Mario A. Nascimento, Alexandre X. Falcão Pages: 102 - 109 | AuGEAS: authoritativeness grading, estimation, and sorting Ayman Farahat, Geoff Nunberg, Francine Chen Pages: 194 - 202 | Detecting similar documents using salient terms James W. Cooper, Anni R. Coden, Eric W. Brown Pages: 245 - 251
Pruning long documents for distributed information retrieval Jie Lu, Jamie Callan Pages: 332 - 339 | On arabic search: improving the retrieval effectiveness via a light stemming approach Mohammed Aljlayl, Ophir Frieder Pages: 340 - 347 | Trajectory queries and octagons in moving object databases Hongjun Zhu, Jianwen Su, Oscar H. Ibarra Pages: 413 - 421
A singer identification technique for content-based classification of MP3 music objects Chih-Chin Liu, Chuan-Sung Huang Pages: 438 - 445 | I/O-efficient techniques for computing pagerank Yen-Yu Chen, Qingqing Gan, Torsten Suel Pages: 549 - 557 | Index compression vs. retrieval time of inverted files for XML documents Norbert Fuhr, Norbert Gövert Pages: 662 - 664
Symbolic photograph content-based retrieval Philippe Mulhem, Joo Hwee Lim Pages: 94 - 101 | "GeoPlot": spatial data mining on video libraries Jia-Yu Pan, Christos Faloutsos Pages: 405 - 412 | The effectiveness study of various music information retrieval approaches Jia-Lien Hsu, Arbee L. P. Chen, Hung-Chen Chen, Ning-Han Liu Pages: 422 - 429

In Need of Ideas? How About:

1. Choose a paper from the above list and try to verify its results. This would involve reading the paper for understanding, and then trying to repeat some of the work they have written up.

1. Read the paper. Find at least two of the papers referenced by this one and read those too.
2. [Part 1] Write up a summary (this forms the basis for your proposal). Descibe what parts you will repeat and why. Explain what you hope to learn / verify from this.
3. [Part 2] Emulate ("copy" by repeating) the work written up in the paper. Include testing and data analysis. Note that you will have to write & run some or all of the programs discussed in the paper.
4. [Part 3] Discuss the paper:
   1. Do you agree with what it says? Why?
   2. What was good about the work / paper/ Why?
   3. What was problematic? Why?
   4. Do you think they achieved their goals? Why?
   5. How do you think they could have done it better?

> Note if you cannot verify the results, there are two possible reasons:

1. You have made a mistake.
2. The authors of the paper have made a mistake.

> The first is more likely, but if you have checked and double-checked your results, then try double checking theirs.

2. Experiment with ways to "reliably" distinguish music from non-music. You will first have to come up with a clear definition of music. Start with something you can easily distinguish such as popular songs. Stick to one file format (like .wav, or MIDI).

3. Experiment with document searches using salient terms. The basic idea is to examine common terms between documents: if they share enough terms, the argument is that the documents are about the same kinds of things. Issues will include finding ways to do this efficiently (imagine how this technique might be useful when applied to thousands of documents).

4. Video: isolate one or two elements of a video file and use these to build a "profile" of the file. See if this allows for any kind of meaningful search.

5.