@dgtlmoon - Its been a year but if you are still interested in this, then this response might be useful.

You can load the file containing the features extracted for each image. The file will be created in the cache directory of your root folder.
This file contains a list of dictionaries, one dictionary per image of your dataset. It is of the form {'img':'xxx', 'cls','123','hist': ''} where img is the name of the image, cls is the class and hist holds the histogram generated for that image.
You can choose one of the element of this list as your query image and then call the function infer which is inside evaluate.py to calculate the similarity distance of the query image and the other images and return the most similar results.

Code mentioned below could be helpful:
from evaluate import infer
samples = cPickle.load(open('D:\Work\CBIR\CBIR-master\cache\histogram_cache-region-n_bin12-n_slice3', "rb", True))
query=samples[13627]
ap,res=infer(query,samples,db=None, sample_db_fn=None, depth=10, d_type='d1')
depth is the number of similar images you want to retrieve and d_type is the type of simialrity metric you want to use.

You might want to modify the function infer inside evaluate to return also the matched image name along with class and distance which it is presently retrieving.

How you get the this figure in Part3: image retrieval (return top 5 of each method)? #5

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