Fundamentals of queueing theory (2nd ed.).
Fundamentals of queueing theory (2nd ed.).
A tutorial on hidden Markov models and selected applications in speech recognition
Readings in speech recognition
International Journal of Computer Vision
The nature of statistical learning theory
The nature of statistical learning theory
The Hierarchical Hidden Markov Model: Analysis and Applications
Machine Learning
Support vector machine pairwise classifiers with error reduction for image classification
MULTIMEDIA '01 Proceedings of the 2001 ACM workshops on Multimedia: multimedia information retrieval
Efficient Color Histogram Indexing for Quadratic Form Distance Functions
IEEE Transactions on Pattern Analysis and Machine Intelligence
SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
A Metric for Distributions with Applications to Image Databases
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-based image classification using a neural network
Pattern Recognition Letters
Image Categorization by Learning and Reasoning with Regions
The Journal of Machine Learning Research
IEEE Transactions on Circuits and Systems for Video Technology
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
3D model comparison using spatial structure circular descriptor
Pattern Recognition
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Histogram is a useful feature for image content analysis and has been widely used in many methods for image categorization. Most of the existing classifiers usually cannot distinguish the effects of different bins in histogram, except for setting different weights. However, these weights are often difficult to be exactly determined in advance. To further mine the information in histogram, in this paper, we propose a method to represent the histogram in another form called quasi-histogram, which can be thought as the state sequence of a Markov chain (MC). By modeling the quasi-histogram of each image as having been stochastically generated by an MC corresponding to its category, we can take the characteristic of each bin into account. Improved image categorization performance can be obtained through combining the results of the traditional classifier with those of MC. Experimental results show the effectiveness of our proposal.