The huge number of images shared on the Web makes effective cataloguing methods for efficient storage and retrieval procedures specifically tailored on the end-user needs a very demanding and crucial issue. In this paper, we investigate the applicability of Automatic Image Annotation (AIA) for image tagging with a focus on the needs of database expansion for a news broadcasting company. First, we determine the feasibility of using AIA in such a context with the aim of minimizing an extensive retraining whenever a new tag needs to be incorporated in the tag set population. Then, an image annotation tool integrating a Convolutional Neural Network model (AlexNet) for feature extraction and a K-Nearest-Neighbours classifier for tag assignment to images is introduced and tested. The obtained performances are very promising addressing the proposed approach as valuable to tackle the problem of image tagging in the framework of a broadcasting company, whilst not yet optimal for integration in the business process.
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Some Native Peoples didn 't want to be photographed because they believed that with every copy of their image, a part of their souls would disappear.By making a copy of an aspect of my existence - a photo, a film, a sound recording, or even a text - my existence goes beyond the immediate here and now. The copy will lead a life of its own. In addition there are young people who process a photo of themselves by smart algorithms in an image-processing app and take this to the plastic surgeon with a request to be operated on this image. Thus we are either lived by producing soulless images, or we strive to become an image of an image. All those soulless images ruin your here and now. Only now I understand that those Native Peoples were right!
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