Optimizing Space Complexity using Color Spaces in CBIR Systems for Medical Diagnosis
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Content based Image Retrieval systems are now used in various automated systems because they largely produce accurate results as they do not depend on the metadata for telling what the image is but rather define it on the basis contents of the image like color, shape, texture and spatial locations. Content based Image retrieval systems have a repository of similar images and when a query image is presented to system it matches similar images in the database. This process aids in various applications like security checks to medical diagnosis. But all CBIR systems in common have to store the images which take a huge space. Here in this work, a unique approach is being devised to reduce the space complexity for a CBIR system used for detecting cervical cancer. When it comes to medical image it is not the question of how to reduce space, but along with it, the original contents of the image also has to be preserved.
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