Hi,
deep convolutional neural networks are a hot topic in image classification. The same networks are aplicable to retrieval as well.
Image classification:
-- Krizhevsky et al.: ImageNet Classification with Deep Convolutional Neural Networks http://www.cs.toronto.edu/~fritz/absps/imagenet.pdf
-- Large Scale Visual Recognition Challenge 2013 http://www.image-net.org/challenges/LSVRC/2013/results.php
Tools:
-- Theano http://benanne.github.io/2014/04/03/faster-convolutions-in-theano.html
-- cuda-convnet https://code.google.com/p/cuda-convnet/
The question is so general which makes it trivial to provide a specific answer. For example, Neural Network has been widely used for years, while it is still being enhanced (for example: Convolutional NN). Among most recent image classification approaches, sparse representation has recently opened a new demanding research area. The following paper is the latest one:
G. Shenghua, I. W. Tsang, M. Yi, "Learning Category-Specific Dictionary and Shared Dictionary for Fine-Grained Image Categorization,", IEEE Transaction on Image Processing, vol.23, no.2, pp.623,634, Feb. 2014
I think classification based on sparse representation is an easier choice for proposing innovations and enhancements, compared to NN and KSVM which are somehow saturated.
A) Recognition Based on Decision-Theoretic Methods
1- Matching
2- Optimum Statistical Classifiers
3- Neural Networks
B) Structural Methods
1- Matching Shape Numbers
2- String Matching
3- Syntactic Recognition of Strings
4- Syntactic Recognition of Trees
For more details Read book Digital Signal and Image Processing Rafael C. Gonzalez, Richard E. Woods
Chapter 12 Object Recognition
May be this will help you
http://www.google.com.pk/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&ved=0CCwQFjAB&url=http%3A%2F%2Fwww.cs.uic.edu%2F~liub%2Fteach%2Fcs583-fall-06%2FCS583-supervised-learning.ppt&ei=Vtd9U_-8D6L30gWFsoGQCA&usg=AFQjCNEdxn39ssWTjBK9kN4Q2g-db-2qDw
http://www.google.com.pk/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=0CCYQFjAA&url=http%3A%2F%2Fwww.cs.uic.edu%2F~liub%2Fteach%2Fcs583-fall-05%2FCS583-unsupervised-learning.ppt&ei=ut19U7TmLIyc0wXVuIGYAQ&usg=AFQjCNEHsktDI2F_iLpH8gS6_5ZBNRXSBg
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