Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Discovering Objects and their Localization in Images
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Using Multiple Segmentations to Discover Objects and their Extent in Image Collections
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Representing shape with a spatial pyramid kernel
Proceedings of the 6th ACM international conference on Image and video retrieval
A tutorial on spectral clustering
Statistics and Computing
Unsupervised Object Discovery: A Comparison
International Journal of Computer Vision
Kernel Entropy Component Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Object discovery is one of the most important applications of unsupervised learning. This paper addresses several spectral clustering techniques to attain a categorization of objects in images without additional information such as class labels or scene descriptions. Due to the fact that background textures bias the performance of image categorization methods, a generic object detector based on some general requirements on objects is applied. The object detector provides rectangular regions of interest (ROIs) as object hypotheses independent of the underlying object class. Feature extraction is simply constrained to these bounding boxes to decrease the influence of background clutter. Another aspect of this work is the utilization of a Gaussian mixture model (GMM) instead of k-means as usually used after feature transformation in spectral clustering. Several experiments have been done and the combination of spectral clustering techniques with the object detector is compared to the standard approach of computing features of the whole image.