"Creating and Exploring a Large Photorealistic Virtual Space," J. Sivic, et al., CVPR 2008.

"Tour the World: building a web-scale landmark recognition engine," CVPR 2009

"World Explorer: Visualizing Aggregate Data from. Unstructured Text in Geo-Referenced Collections," Ahern et al., JCDL 2007.

"On Spectral Clustering: Analysis and an algorithm", Andrew Y. Ng, Michael I. Jordan, Yair Weiss, NIPS 2001

"Normalized Cuts and Image Segmentation", Jianbo Shi and Jitendra Malik, Trans. PAMI 2000.

R. Fergus, P. Perona, and A. Zisserman. Object class recognition by unsupervised scale-invariant learning, In Proc. CVPR, Jun 2003.

In this paper, author propose a image recognize system for determinate whether an object is in the query image. The system can be break into three parts: representation, learning, and recognition. Representation means break image into features, and used these features to represent an object. An object is modeled as a constellation parts, where appearance, shape, relative scale and occlusion are all considered by the model. EM algorithm is used as an unsupervised learning method in this paper. And recognition is done by compare the probability ratio of query image having object present to a threshold. The result of this system is significantly better than the baseline. But the scale of the dataset they used is maybe not large enough, one of the dataset only contain couple hundred images. Also they aligned the images make them facing the same way before learning.
This paper provide a complete probability model to model object in a image. It is kind of hard to digest at the first glance. The model consider several parts including scales, but not rotation. Therefore they need to preprocessing the image before learning. The author suggest to improve the system by adding rotation invariance in the future works.

"Non-linear dimensionality reduction techniques for classification and visualization," Vlachos, et al., KDD 2002.

In this paper, they use non-linear dimensionality reduction techniques to address two problem. One is visualization of high dimensional data, another is curse-of dimensionality problem of classification. In problem one, it propose that LLE and Isomap can not preform well when data are sample from different clusters. They improve the solution by keep distance to both nearest and farthest object when mapping. They solve the second problem by introduce two techniques, weightedIso and Iso+Ada which modifying the transformation performed by the Isomap and produce homogeneous neighborhoods in reduced space. The experiment shows that it perform as well as other method but in a much lower dimensional.