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Content Based Image Retrieval by Using Color Descriptor and Discrete Wavelet Transform

  • Image & Signal Processing
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

Due to recent development in technology, the complexity of multimedia is significantly increased and the retrieval of similar multimedia content is a open research problem. Content-Based Image Retrieval (CBIR) is a process that provides a framework for image search and low-level visual features are commonly used to retrieve the images from the image database. The basic requirement in any image retrieval process is to sort the images with a close similarity in term of visually appearance. The color, shape and texture are the examples of low-level image features. The feature plays a significant role in image processing. The powerful representation of an image is known as feature vector and feature extraction techniques are applied to get features that will be useful in classifying and recognition of images. As features define the behavior of an image, they show its place in terms of storage taken, efficiency in classification and obviously in time consumption also. In this paper, we are going to discuss various types of features, feature extraction techniques and explaining in what scenario, which features extraction technique will be better. The effectiveness of the CBIR approach is fundamentally based on feature extraction. In image processing errands like object recognition and image retrieval feature descriptor is an immense among the most essential step. The main idea of CBIR is that it can search related images to an image passed as query from a dataset got by using distance metrics. The proposed method is explained for image retrieval constructed on YCbCr color with canny edge histogram and discrete wavelet transform. The combination of edge of histogram and discrete wavelet transform increase the performance of image retrieval framework for content based search. The execution of different wavelets is additionally contrasted with discover the suitability of specific wavelet work for image retrieval. The proposed algorithm is prepared and tried to implement for Wang image database. For Image Retrieval Purpose, Artificial Neural Networks (ANN) is used and applied on standard dataset in CBIR domain. The execution of the recommended descriptors is assessed by computing both Precision and Recall values and compared with different other proposed methods with demonstrate the predominance of our method. The efficiency and effectiveness of the proposed approach outperforms the existing research in term of average precision and recall values.

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Authors and Affiliations

  1. Department of Computer Science, National Textile University, Faisalabad, Pakistan

    Rehan Ashraf, Mudassar Ahmed & Sohail Jabbar

  2. Department of Computer Engineering, Bahria University, Islamabad, Pakistan

    Shehzad Khalid

  3. Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, Republic of Korea

    Awais Ahmad

  4. Department of Computer Engineering, Kyungpook National University, Daegu, South Korea

    Sadia Din

  5. Department of Embedded System Engineering, Incheon National University, Incheon, South Korea

    Gwangil Jeon

Authors
  1. Rehan Ashraf
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  2. Mudassar Ahmed
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  4. Shehzad Khalid
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  7. Gwangil Jeon
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Correspondence to Rehan Ashraf.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Conflict of interests

Authors Rehan Ashraf, Mudassar Ahmed, Sohail Jabbar, Shehzad Khalid, Awais Ahmad, Sadia Din, and Gwangil Jeon declare that they have no conflict of interest.

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This article is part of the Topical Collection on Image & Signal Processing

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Ashraf, R., Ahmed, M., Jabbar, S. et al. Content Based Image Retrieval by Using Color Descriptor and Discrete Wavelet Transform. J Med Syst 42, 44 (2018). https://doi.org/10.1007/s10916-017-0880-7

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