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Major developments in image capture and image editing technologies have resulted in enormous increase of near duplicate data generation. This coupled with increased use of internet and availability of storage has led to large databases of such images being available. Hence, near-duplicate image detection and retrieval is an essential operation to refine image search results for effective user investigation. We have compared and analyzed global color features for near duplicate detection and retrieval using three frameworks a) Entire Image-Based framework b)Segment based Framework c) Grid based framework. We have studied the method in terms of the type of near duplicate images retrieved like images based on photo-metric variation, geometric variation or non-identical near duplicates etc. To facilitate a significant assessment, we generated 1000 images by using randomly selected individual images from UKBench and California ND collection, each of which is then digitally transformed using 47 different alterations. After retrieving the near duplicate images from the database we aim at constructing an image dependency tree to discover the dependency among the images and to identify the transformational relationship in the set of retrieved near duplicates.