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Improving Image Recovery Rates in Big Data


Improving Image Recovery Rates in Big Data

The current problem with identifying objectionable images is that there are an enormous number of images on the internet that require analysing. forensic investigators are currently using two techniques for identification, namely; content based image retrieval (cbir) that uses visual aspects of an image for identification and concept based image indexing (cbii) that uses the metadata of the image for identification. the current challenge for investigators is to resolve the dilemma of one technique that is accurate, but very slow against the other that is inaccurate, but very fast for image retrieval. the utilization of both techniques to solve the problem is cost prohibitive in terms of time and resources in professional practice. our research was to investigate other methods of identifying images, which resulted with a ‘hashing’ based methodology that had the potential for providing an improvement in performance overall.




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