Monday, 11 April 2016

Content based image retrieval(CBIR) 00--Use CBIR to find similar images of ukbench

    Content based image retrieval(CBIR), also called as query by image content(QBIC), google search by image and TinEye maybe are the famous example in our daily live. In short, CBIR search the images based on the content of the image, not the name,date,meta data or other info.

    I study how to implement CBIR from PyImageSearch Gurus, the algorithms are almost the same, but my codes are written by c++, build on top of opencv, hdf5, armadilloboost, rapidjson. I pick c++ but not python(PyImageSearch use python) for this project because

1 : c++ suit for building stand alone package
2 : I like c++

    These series of post would not discuss the implementation details of the codes(codes located at github) but summarize the keys I learn from the CBIR lessons of PyImageSearch Gurus.

    The keys of this CBIR system

1 : Feature detector--kaze
2 : Feature descriptor--kaze
3 : Bag of visual words
4 : Data structure(hdf5, inverted index)
5 : Create Code book(I prefer kmeans)
6 : Quantization(build a histogram)
7 : Tf-idf(Term frequency and inverse document frequency)
8 : Spatial verification
9 : Evaluation

   ukbench contain 6376 images, it would be a tedious job to find relevant images by human, this is why we need CBIR to save us from this kind of labor.  Before I begin to summarize the keys of this CBIR system, I would post some examples, a picture is worth a thousand words.

Case 1 : Find similar image of the camera(pic00) within ukbench


Search result of pic00

Case 2 : Find similar image of the toy(pic01) within ukbench


Search result of pic01
    The most similar image are shown at the first row. Until now, I think it should be clear enough to show what are CBIR intent to solve. We could use it to deal with a lot of problems, like object recognition(however, cnn is state of the art when I writing this post), search web site with the image(like google and TinEye), remove duplicate images and so on.

    On next post, I would record part of the flows of this CBIR system, write down how to use the codes located on github(without explanation of  implementation details).

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