# Exhaustive Nearest Neighbor Search vs KNN

I have two lists of feature vectors calculated from pre-trained CNN for image retrieval task:

Query: FV_Q and Reference FV_R.

>>> 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 mistake somewhere in my feature vector comparison?

Cheers,