Genetic –Based Face Retrieval Using Statistical Features | ||
| Ibn Al-Haitham Journal For Pure And Applied Science | ||
| Article 1, Volume 29, Issue 1, April 2016, Pages 254-266 | ||
| Authors | ||
| Wathiq N. Abdullah; Yossra H. Ali | ||
| Abstract | ||
| Content-based image retrieval has been keenly developed in numerous fields. This provides more active management and retrieval of images than the keyword-based method. So the content based image retrieval becomes one of the liveliest researches in the past few years. In a given set of objects, the retrieval of information suggests solutions to search for those in response to a particular description. The set of objects which can be considered are documents, images, videos, or sounds. This paper proposes a method to retrieve a multi-view face from a large face database according to color and texture attributes. Some of the features used for retrieval are color attributes such as the mean, the variance, and the color image's bitmap. In addition, the energy, and the entropy which based on the gray level values in an image is too considered as the features. In addition to statistical approaches, models of artificial intelligence produce a desirable methodology that enhances performance in information retrieval systems, and the genetic algorithm depicts one of them. The GA is preferred for its power and because it can be used without any specific information of the domain. The experimental results conclude that using GA gives a good performance and it decreases the average search time to (60.15 milliseconds) compared with (722.25milliseconds) for traditional search. | ||
| Keywords | ||
| CBIR; Face Retrieval; genetic algorithm; Statistical Features; Wavelet | ||
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