A genetic-based prototyping for automatic image annotation

The explosive growth of visual and textual data has led to urgent requirements in management and understanding of digital content. Developing optimal solutions to allow access to and mining such data in modern applications is crucial. Image annotation or tagging, is a process which produces words, keywords or comments to an image. In the nearest neighbor-based automatic image annotation, a training set T is given to a classifier for classifying new prototypes. In practice, T contains useless images for the image annotation task, that is, superfluous prototypes, which can be noisy or redundant; therefore a process is needed to discard them from T. In this paper, a genetic-based prototyping for automatic image annotation is proposed. We first adopt a genetic-based prototyping algorithm to obtain optimal prototype from images. Then, for a given query image, its neighbor images are retrieved from the optimal prototype gained, and to generate its candidate tags some methods such as voting are used. Experimental results on standard benchmark datasets show that the proposed method achieves order of magnitude speedups over the related techniques and obtains much better annotate quality as well. (C) 2017 Elsevier Ltd. All rights reserved.