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Textile Retrieval Based on Image Content from CDC and Webcam Cameras in Indoor Environments.

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This study introduces a new method for retrieving textile images indoors, aiding law enforcement. The pipeline effectively identifies and matches textile regions, outperforming existing deep learning approaches.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Pattern Recognition

Background:

  • Textile-based image retrieval is valuable for identifying related indoor scenes, particularly for law enforcement evidence matching.
  • Existing methods may lack efficiency or accuracy in complex indoor environments.

Purpose of the Study:

  • To develop a novel pipeline for searching and retrieving textile images within indoor scenes.
  • To establish a robust method for matching textile regions using feature descriptors and a new dataset.

Main Methods:

  • Utilizing MSER on high-pass filtered RGB, HSV, and Hue channels to detect textile regions.
  • Employing a combination of Histogram of Oriented Gradients (HOG) and Color Histogram (HCLOSIB) with correlation distance for feature description and matching.
  • Introducing the TextilTube dataset with 1913 labeled textile regions across 67 classes.

Main Results:

  • The proposed pipeline achieved 84.94% success in 40 nearest neighbor coincidences.
  • A precision of 37.44% was obtained for the first coincidence, surpassing current deep learning methods.
  • Experimental validation confirms the pipeline's effectiveness for indoor textile image retrieval.

Conclusions:

  • The developed pipeline offers an effective solution for textile-based image retrieval in indoor settings.
  • The combination of MSER, HOG, HCLOSIB, and the TextilTube dataset provides a strong foundation for future research and applications.