Version 1
: Received: 30 November 2018 / Approved: 3 December 2018 / Online: 3 December 2018 (13:33:45 CET)
How to cite:
Nazir, A.; Nazir, K. An Efficient Image Retrieval Based on Fusion of Low-Level Visual Features. Preprints2018, 2018120022. https://doi.org/10.20944/preprints201812.0022.v1
Nazir, A.; Nazir, K. An Efficient Image Retrieval Based on Fusion of Low-Level Visual Features. Preprints 2018, 2018120022. https://doi.org/10.20944/preprints201812.0022.v1
Nazir, A.; Nazir, K. An Efficient Image Retrieval Based on Fusion of Low-Level Visual Features. Preprints2018, 2018120022. https://doi.org/10.20944/preprints201812.0022.v1
APA Style
Nazir, A., & Nazir, K. (2018). An Efficient Image Retrieval Based on Fusion of Low-Level Visual Features. Preprints. https://doi.org/10.20944/preprints201812.0022.v1
Chicago/Turabian Style
Nazir, A. and Kashif Nazir. 2018 "An Efficient Image Retrieval Based on Fusion of Low-Level Visual Features" Preprints. https://doi.org/10.20944/preprints201812.0022.v1
Abstract
Due to an increase in the number of image achieves, Content-Based Image Retrieval (CBIR) has gained attention for research community of computer vision. The image visual contents are represented in a feature space in the form of numerical values that is considered as a feature vector of image. Images belonging to different classes may contain the common visuals and shapes that can result in the closeness of computed feature space of two different images belonging to separate classes. Due to this reason, feature extraction and image representation is selected with appropriate features as it directly affects the performance of image retrieval system. The commonly used visual features are image spatial layout, color, texture and shape. Image feature space is combined to achieve the discriminating ability that is not possible to achieve when the features are used separately. Due to this reason, in this paper, we aim to explore the low-level feature combination that are based on color and shape features. We selected color moments and color histogram to represent color while shape is represented by using invariant moments. We selected this combination, as these features are reported intuitive, compact and robust for image representation. We evaluated the performance of our proposed research by using the Corel, Coil and Ground Truth (GT) image datasets. We evaluated the proposed low-level feature fusion by calculating the precision, recall and time required for feature extraction. The precision, recall and feature extraction values obtained from the proposed low-level feature fusion outperforms the existing research of CBIR.
Keywords
Image retrieval, color features, shape features, low-level features combination
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
