Content-Based Image Retrieval Optimization by Differential Evolution
| Rodzaj publikacji: | Konferencja | ||
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| Rok: | 2016 | ||
| Autorzy: |
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| Tytuł książki: | Proceedings of 2016 IEEE Congress on Evolutionary Computation (CEC) | ||
| Strony: | 86-93 | ||
| Adres: | Vancouver BC, Canada | ||
BibTex: |
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| Abstrakt: | In this paper we present a new method for contentbased searching large
image databases by comparing content of a query image and images
stored in a database. The algorithm consists of three main steps:
feature extraction, indexing and system learning. The feature extraction
stage is based on two types of features (SURF keypoints and color).
For indexing we use the k-means algorithm and for system learning
we applied differential evolution. This last step is very important,
and significantly improves the results. The presented algorithm can
be easily modified, by changing its components (feature extractor
or clustering algorithm). |
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