Score distribution approach to automatic kernel selection for image retrieval systems

Authors:
Anca Doloc-Mihu;Vijay V. Raghavan
Affiliations:
University of Louisiana at Lafayette, Lafayette, LA;University of Louisiana at Lafayette, Lafayette, LA
Venue:
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
Year:
2006

Citing 7
Cited 0

The LIVE-project: retrieval experiments based on evaluation viewpoints

SIGIR '85 Proceedings of the 8th annual international ACM SIGIR conference on Research and development in information retrieval
An introduction to support Vector Machines: and other kernel-based learning methods

An introduction to support Vector Machines: and other kernel-based learning methods
Maximum likelihood estimation for filtering thresholds

Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Color image quantization for frame buffer display

SIGGRAPH '82 Proceedings of the 9th annual conference on Computer graphics and interactive techniques
How to Complete Performance Graphs in Content-Based Image Retrieval: Add Generality and Normalize Scope

IEEE Transactions on Pattern Analysis and Machine Intelligence
Using score distribution models to select the kernel type for a web-based adaptive image retrieval system (AIRS)

CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
Support vector machines for histogram-based image classification

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper introduces a kernel selection method to automatically choose the best kernel type for a query by using the score distributions of the relevant and non-relevant images given by user as feedback. When applied to our data, the method selects the same best kernel (out of the 12 tried kernels) for a particular query as the kernel obtained from our extensive experimental results.