Other types of image search

http://www.xcavator.net/
http://www.fotosearch.com/
http://www.yangsky.com/products/picseer/index.htm

Some famous image search engines

http://www.faganfinder.com/img/
http://www.search-engine-index.co.uk/Images_Search/
http://images.google.com/
http://www.picsearch.com/
http://www.altavista.com/image/default
http://www.ask.com/?tool=img
http://www.exalead.com/image/results?q=
http://www.pixsy.com/
http://www.netvue.com/
http://www.airtightinteractive.com/projects/simple_image_search/app/
http://www.ithaki.net/images/
http://yotophoto.com/

Learning from Small Number of Examples

In this project, there is a need that user may select several images as query images. I choose discrimintive model as our approach.
Based on the libsvm provided by Prof. Lin Chih-Jen, the algorithm is listed as below:
1. Read the checked images and un-checked images.
2. The checked images are regarded as positive examples. And the un-checked images and other images in dataset are regarded as pseudo-negative examples.
3. If the number of positive examples is N, then 2N pseudo-negative examples are generated.
4. For each loop do
- Construct a bag (training set) that contains N positive examples and 2N pseudo-negative examples.
- Choose the whole image dataset as testing set.
- Call svm_train to generate the model. Due to some parameters (g, C) are adjustable, I use the tools provided by Prof. Lin Chih-Jen to find out the best performance.
- Use the predict call to generate the classification results. The prob. parameter must be set to active.
- Collect the probability table.
5. Use MIN fusion to obtain the final result.
6. Filter out the positive examples in descending order and output to system.