Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mode-Finding for Mixtures of Gaussian Distributions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift Is a Bound Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast nonparametric clustering with Gaussian blurring mean-shift
ICML '06 Proceedings of the 23rd international conference on Machine learning
Gaussian Mean-Shift Is an EM Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
The estimation of the gradient of a density function, with applications in pattern recognition
IEEE Transactions on Information Theory
Clustering using elements of information theory
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
An experimental study of color-based segmentation algorithms based on the mean-shift concept
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Comparative study on information theoretic clustering and classical clustering algorithms
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Information-theoretic clustering: A representative and evolutionary approach
Expert Systems with Applications: An International Journal
Representative cross information potential clustering
Pattern Recognition Letters
Regularized discriminant entropy analysis
Pattern Recognition
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This paper develops a new understanding of mean shift algorithms from an information theoretic perspective. We show that the Gaussian blurring mean shift (GBMS) directly minimizes the Renyi's quadratic entropy of the dataset and hence is unstable by definition. Further, its stable counterpart, the Gaussian mean shift (GMS), minimizes the Renyi's ''cross'' entropy where the local stationary solutions are modes of the dataset. By doing so, we aptly answer the question ''What does mean shift algorithms optimize?'', thus highlighting naturally the properties of these algorithms. A consequence of this new understanding is the superior performance of GMS over GBMS which we show in a wide variety of applications ranging from mode finding to clustering and image segmentation.