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Hi to all, I started to work on a hadoop-based project. In our application, there are a huge number of images with a regular pattern, differing in 4 parts/blocks. System takes an image as input and looks for a similar image, considering if all these 4 parts match. (System finds all the matches, even after finding one). Each of these parts are independent, result of each part computed separately, these are printed on the screen and then an average matching percentage is calculated from these. (I can write more detailed information if needed) Could you suggest a structure? or any ideas to have a better result? Images can be divided into 4 parts, I see that. But folder structure of images are important and I have no idea with that. Images are kept in DB (can be changed, if folder structure is better) Is two stage of map-reduce operations better? First, one map-reduce for each image, then a second map-reduce for every part of one image. But as far as I know, the slowest computation slows down whole operation. Thanks in advance.. -- View this message in context: http://www.nabble.com/Hadoop-with-image-processing-tp19994780p19994780.html Sent from the Hadoop lucene-users mailing list archive at Nabble.com.
