I have two lists of feature vectors calculated from pre-trained CNN for image retrieval task:
FV_Q and Reference
>>> FV_R.shape (3450, 128) >>> FV_Q.shape (3450, 128)
I am a little confused between the concept of exhaustive nearest neighbor search and k-nearest neighbor search.
In python, I use
from sklearn.neighbors import KDTree to extract top
k = 5 similar images from the reference database, given the query image!
Can somebody explain if there might be any similarities/differences between these two concepts?
Am I making a mistake somewhere in my feature vector comparison?