I want to use the FLANN instead of QuantiserTask class for visual word assignment. I want to know how the data is organized in image feature file xxx.jpg.fv and mirflickr-sift-fastkmeans-1000000-new.voc so that I can read them by C++. Any suggestion?
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Note all binary numbers are written in Java format (i.e. Big Endian) - if you want to read them in C++ on a little endian machine (i.e. any PC with a x86/64 type processor), you'll need to swap the byte order. The FV files are lists of local features, with the following header structure:
The features are actually dependent on the type of local feature extracted... For DoG/SIFT each one looks like this:
The quantiser file format depends on what clustering mode you chose and what precision (byte or int). Assuming you chose KMeans or Fast-KMeans with byte precision (the default), the .voc file has the following format:
I'd be interested to know whether you find FLANN any faster... In theory there shouldn't be much difference as imageterrier is using the same fundamental algorithms for cluster assignment.
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