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I am a newbie in Matlab field. And i want to learn more about methodology to comparing 2 images to know the similarity between them.
I need more information in international journal / international proceeding, book or another reprort that describe about it.
I Will use it as my literature study.
Is there any suggestion what is the similar journal,book or proceeding that has discussed about it? If has, please include the title and link of them..
Thank You for the attention.
For journals I would recommend the IEEE Transactions on Image Processing:
http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=83
This is a good general intro from MIT:
http://www.mit.edu/~ka21369/Imaging2012/tannenbaum.pdf
You need to define "similarity" better.
In the image compression sense, similarity is a function of the pixel-wise difference between the images (PSNR, and other metrics).
In a computer vision sense, you would want to see if the two images contain similar content such as objects or scenes. I would recommend using Google Scholar for that.
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I've been told about car's plate image database that are avaliable on the web for free download to develop image processing and automatic number plate recognition algorithms, does anyone have a link to download or at least some keywords to search on the web?
If it's not legal or is there any ethic issues i would thank if you notice me.
It's perfectly legal to do so, as long as the images are CC (Creative Commons) licensed, or you have permission of the website owner to do.
A quick search for number plate image database yields some results:
Examples of test images
Academic Document (More examples of number plates)
Good small library of number plates.
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I'm working on a school project on product analysis which is based on sentimental analysis. I've been looking for a training dataset for quite a some time now and what I've been able to find so far is a dataset for movie reviews. My question is, can I use this dataset for training the classifier, i.e. will it have an effect on the accuracy of classification? If so, does anyone here know where I can get a free dataset for product reviews?
I am assuming you are using some textual model like the bag of words model.
From my experiments, you usually don't get good results when changing from one domain to another (even if the train data set and the test are all products, but of different categories!).
Think of it logically, an oven that gets hot quickly usually indicate a good product. Is it also the same for laptops?
When I experimented with it a few years ago I used amazon comments as both train set and also to test my algorithms.
The comments are short and informative and were enough to get ~80% accuracy. The 'ground' truth was the stars system, where 1-2 stars were 'negative', 3 stars - 'neutral', and 4-5 stars 'positive'.
I used a pearl script from esuli.it to crawl amazon's comments.
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I hoping to create an application that would listen to ambient sound and detect if music is being played. It is not important to identify the music being played; just detecting that some music is being played is enough.
I looked around for existing solutions but couldn't find any. Does anyone know algorithms that I can use to solve this problem? If source code is available, all the better.
I found are a couple of academic papers and implemented solutions suggested in them. But the results I obtained were not satisfactory.
PS:
i) It would be a bonus if the algorithm is not computationally intensive; if algorithm is completely in time-domain that would be wonderful. ii) It is okay if the solution is not very accurate; occasional false-positives are okay.
Under the assumption that music is made of a bunch of chords instead of single pitch (like monophonic MIDI), multiple pitches at the same time (aka, the chords) may be a good candidate to be detected and differentiated from pure noise. Actually there is a very good Harmony Progression Analyser software package in which chords are detected based on a chromagram. Hope it helps.
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Our site allows users to enter the company they work for as a free form text entry.
Historically we gathered around a few millions of unique entries. Since we put no constraints we ended up with a lot of variations, typos (e.g. over 1000 distinct entries just for McDonald's)
We realized we could provide our users with a great feature if only we could tie these variations together. We compiled a clean list of companies as a starting point using various online sources [Dictionary]
Now, we're trying to find out a best way to deal with the user data source. We thought about assigning some similarity score:
- comparing each entry with [Dictionary], calculating a lexical distance (possibly in Hadoop job)
- taking advantage of some search database (e.g. Solr)
And associate the user enter text this way.
What we're wondering is did anyone go through similar "classification" exercise and could share any tips?
Thanks,
Piotr
I'd use simple Levenshtein distance (http://en.wikipedia.org/wiki/Levenshtein_distance).
A few millions entries - you should be able to process it easily on one computer (no hadoop, or other heavy-weight tools).
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I'm new to web programming. Where is the best place online can I find examples of algorithms created to solve specific problems ie. reputation system for a user controlled forum.
On my opinion a good introduction to this issue you can fing in this book:
Paperback: 336 pages
Publisher: Yahoo Press; 1 edition (March 16, 2010)
Language: English
ISBN-10: 059615979X
ISBN-13: 978-0596159795
Table of Contents:
Part I. Reputation Defined and Illustrated
Reputation Systems Are Everywhere
A (Graphical) Grammar for Reputation
Part II. Extended Elements and Applied Examples
Building Blocks and Reputation Tips
Common Reputation Models
Planning Your System’s Design
Objects, Inputs, Scope, and Mechanism
Displaying Reputation
Using Reputation: The Good, The Bad, and the Ugly
Application Integration, Testing, and Tuning
Case Study: Yahoo! Answers Community Content Moderation
Here you can buy it: http://www.amazon.com/Building-Reputation-Systems-Randy-Farmer/dp/059615979X