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Color Image Retrieval Method Using Low Dimensional Salient Visual Feature Descriptors for IoT Applications.

Naushad Varish1, Priyanka Singh2, Prannoy Tugiti2

  • 1Department of Computer Science and Engineering, GITAM (Deemed to be University), Hyderabad 502329, Telangana, India.

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Summary
This summary is machine-generated.

This study introduces a new image retrieval system using combined color and texture features for faster searching in large datasets. The proposed method significantly outperforms existing algorithms.

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Area of Science:

  • Computer Science
  • Information Technology
  • Digital Image Processing

Background:

  • Rapid increase in digital data from sources like smartphones and IoT necessitates efficient image storage and retrieval.
  • Large-scale image databases pose challenges for effective searching and data management.
  • Low-dimensional feature descriptors are crucial for accelerating image retrieval processes.

Purpose of the Study:

  • To develop an efficient image retrieval system for large-scale databases.
  • To construct a low-dimensional feature descriptor by integrating color and texture information.
  • To enhance the speed and accuracy of image searching and retrieval.

Main Methods:

  • A novel feature extraction approach combining color and texture is proposed.
  • Color features are quantified from a preprocessed HSV color image.
  • Texture features are extracted from the V-plane of the HSV image using Sobel edge detection, Discrete Cosine Transform (DCT), and Gray Level Co-occurrence Matrix (GLCM).

Main Results:

  • The proposed image retrieval scheme was validated on a benchmark dataset.
  • Experimental results demonstrated superior performance compared to ten state-of-the-art image retrieval algorithms.
  • The integrated color and texture descriptor proved effective in speeding up retrieval.

Conclusions:

  • The proposed system offers a significant improvement in image retrieval efficiency.
  • Integrating color and texture features provides a robust method for creating low-dimensional descriptors.
  • This approach is highly effective for managing and searching vast image repositories.