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concatenate CNN features and image features #33
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Hey, We have a desriptor function describe() in ImageDescriptor.py which returns flattened descriptive array of an image. You can concat its output with output of Flatten layer of your CNN model. This would be a completely different description array for an image and you can evaluate its performance through different experiments. Please share your results with me, if you try such experiments! |
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Hey. Of course, you can't directly concat all images with their features using our numpy files. Because as I explained, those numpy files of images were not generated in order to concat with any other feature. So; if you want to concat an image and it's feature array in a single numpy array, you should write a new function which processes images one by one. Because you can't make sure of each read image from directory corresponds to its numpy or feature array. In brief, you should not use our image_to_matrix() function as it is. You can change that function to take a single image(not a directory) and return it's numpy version as we do. But do not try to process all directory at once. So, you wont have any shuffle problem as well, if you process each image one by one instead of all at once like us. You can directly use output of our describe() function, because it processes a single image and returns it description array. So, we are fine there. Hope it helps :) |
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Hey,
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Hello, do you have experiments to concatenate CNN features and image features(haze、sky、contrast、histogram\sharpness)?
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