Effective image and video mining: an overview of model-based approaches
MDM '05 Proceedings of the 6th international workshop on Multimedia data mining: mining integrated media and complex data
An empirical investigation of user term feedback in text-based targeted image search
ACM Transactions on Information Systems (TOIS)
Generalized Robust Conjoint Estimation
Marketing Science
An Evaluation of the Robustness of MTS for Imbalanced Data
IEEE Transactions on Knowledge and Data Engineering
Evolving Committees of Support Vector Machines
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Improved feature reduction in input and feature spaces
Pattern Recognition
The forecasting model based on modified SVRM and PSO penalizing Gaussian noise
Expert Systems with Applications: An International Journal
Combining functional networks and sensitivity analysis as wrapper method for feature selection
Expert Systems with Applications: An International Journal
Parameterized attribute reduction with Gaussian kernel based fuzzy rough sets
Information Sciences: an International Journal
Feature subset selection using improved binary gravitational search algorithm
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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The success of a multimedia information system depends heavily on the way the data is represented. Although there are “natural” ways to represent numerical data, it is not clear what is a good way to represent multimedia data, such as images, video, or sound. In this paper, we investigate various image representations where the quality of the representation is judged based on how well a system for searching through an image database can perform—although the same techniques and representations can be used for other types of object detection tasks or multimedia data analysis problems. The system is based on a machine learning method used to develop object detection models from example images that can subsequently be used for examples to detect—search—images of a particular object in an image database. As a base classifier for the detection task, we use support vector machines (SVM), a kernel-based learning method. Within the framework of kernel classifiers, we investigate new image representations/kernels derived from probabilistic models of the class of images considered and present a new feature selection method which can be used to reduce the dimensionality of the image representation without significant losses in terms of the performance of the detection-search-system.