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Texture Feature Extraction Based on Fractional Mask Convolution with Cesáro Means for Content-Based Image Retrieval

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 7458)

Abstract

This paper introduces a texture features extraction technique for content-based image retrieval using fractional differential operator mask convolution with Cesáro means. We propose one general fractional differential mask on eight directions for texture features extraction. Image retrieval based on texture features is getting unusual concentration because texture is an important feature of natural images. Experiments show that, the capability of texture features extraction by fractional differential-based approach appears efficient to find the best combination of relevant retrieved images for different resolutions. To compare the performance of image retrieval method, average precision and recall are computed for query image. The results showed an improved performance (higher precision and recall values) compared with the performance using other methods of texture extraction.

Keywords

  • Fractional calculus
  • fractional differential
  • Cesáro means
  • fractional mask
  • texture segmentation
  • content based image retrieval

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Jalab, H.A., Ibrahim, R.W. (2012). Texture Feature Extraction Based on Fractional Mask Convolution with Cesáro Means for Content-Based Image Retrieval. In: Anthony, P., Ishizuka, M., Lukose, D. (eds) PRICAI 2012: Trends in Artificial Intelligence. PRICAI 2012. Lecture Notes in Computer Science(), vol 7458. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32695-0_17

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  • DOI: https://doi.org/10.1007/978-3-642-32695-0_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32694-3

  • Online ISBN: 978-3-642-32695-0

  • eBook Packages: Computer ScienceComputer Science (R0)