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End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
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Published on: December 15, 2023

SEMI-SUPERVISED OBJECT RECOGNITION USING STRUCTURE KERNEL.

Botao Wang1, Hongkai Xiong, Xiaoqian Jiang

  • 1Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.

Proceedings. International Conference on Image Processing
|May 14, 2013
PubMed
Summary
This summary is machine-generated.

We introduce a novel "structure kernel" for improved object recognition in computer vision. This new kernel enhances the accuracy of part-based models, outperforming existing standard kernel methods.

Keywords:
Object recognitiondata miningimage featureskernelmachine learning

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Area of Science:

  • Computer Vision
  • Machine Learning

Background:

  • Object recognition is crucial in computer vision.
  • Part-based models offer flexible object representations but face training challenges and limitations with standard kernels.

Purpose of the Study:

  • To propose a novel positive definite kernel, the "structure kernel", for enhanced similarity measurement in part-based object models.
  • To generalize the discriminant capability of local kernels for complex object representations.

Main Methods:

  • Developed a "structure kernel" with three components: global visual similarity, corresponding part similarity, and spatial configuration similarity.
  • Applied the kernel to measure similarity between part-based represented objects.

Main Results:

  • The proposed structure kernel demonstrated higher accuracy compared to state-of-the-art approaches using standard kernels.
  • Successfully generalized discriminant capabilities to complex part-based object models.

Conclusions:

  • The structure kernel offers a significant advancement for object recognition tasks.
  • This kernel provides a more effective way to measure similarity in part-based object models, leading to improved recognition accuracy.