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Improving Image Retrieval Through a Collection of Fast and Simple Visual Features Extraction |
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PP: 65-76 |
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Author(s) |
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Ismail Hmeidi,
Amer Al-Badarenah,
Abdulrahman Al-Molegi,
Izzat Alsmadi,
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Abstract |
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| In this paper, we experimented a large set of feature extraction methods with fast and simple computation approaches. Some
of those methods were proposed in different areas and domains and we thought of evaluating their ability in enhancing the image
retrieval process. Several low-level image features are selected as part of our image retrieval system. Examples of feature extraction
methods used include features related to RGB and HSV color schemes, color and texture features and finally features collected through
the open source MaZda software. Based on conducting experiments, our proposed method for extracting features with less computation
time and improved results in terms of retrieving accuracy. Similarity measures such as: Euclidean, Chebyshev and Manhattan were also
used to measure distances between subject image and database images. A dataset of 1000 images from COREL database is used and
classified into 10 different categories. Precision and recall metrics are used to evaluate the performance of the retrieval process. The final
results showed a good, qualified image retrieval system that is capable in retrieving a good number of relevant images using color and
texture features with normalized RGB histogram. Retrieving precision and recall were 78% and 51% respectively. In terms of similarity
measures, Euclidean is shown to be the best of those evaluated for image classification then Chebyshev and finally Manhattan. |
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