Integrating Multiple Classifiers In Visual Object Detectors Learned From User Input

@inproceedings{Jaimes2000IntegratingMC,
  title={Integrating Multiple Classifiers In Visual Object Detectors Learned From User Input},
  author={Alejandro Jaimes and Shih-Fu Chang},
  year={2000}
}
There have been many recent efforts in contentbased retrieval to perform automatic classification of images/visual objects. Most approaches, however, have focused on using individual classifiers. In this paper, we study the way in which, in a dynamic framework, multiple classifiers can be combined when applying Visual Object Detectors. We propose a hybrid classifier combination approach, in which decisions of individual classifiers are combined in the following three ways: (1) classifier fusion… CONTINUE READING

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References

Publications referenced by this paper.
SHOWING 1-10 OF 11 REFERENCES

Automatic selection of visual features and classifiers

  • Storage and Retrieval for Media Databases
  • 1999
VIEW 7 EXCERPTS
HIGHLY INFLUENTIAL

MLC++: a machine learning library in C++

  • Proceedings Sixth International Conference on Tools with Artificial Intelligence. TAI 94
  • 1994
VIEW 3 EXCERPTS
HIGHLY INFLUENTIAL

On image classification: city vs. landscape

  • Proceedings. IEEE Workshop on Content-Based Access of Image and Video Libraries (Cat. No.98EX173)
  • 1998
VIEW 1 EXCERPT

Combinations of weak classifiers

  • IEEE Trans. Neural Networks
  • 1996
VIEW 1 EXCERPT