I am currently reading the research paper Image Crowd Counting Using Convolutional Neural Network and Markov Random Field by Kang Han, Wanggen Wan, Haiyan Yao, and Li Hou.
I did not understand the following context properly:
Formally, the Markov random field framework for the crowd counting can be defined as follows (we follow the notation in ). Let P be the set of patches in an image and C be a possi- ble set of counts. A counting c assigns a count c p ∈ C to each patch p ∈ P. The quality of a counting is given by an energy function:
E(c) = ∑ D p (c p ) + ∑ p∈P V (c p − c q ) . . . (2) (p,q)∈N
where N are the (undirected) edges in the four-connected image patch graph. D p (c p ) is the cost of assigning count c p to patch p, and is referred to as the data cost. V (c p −c q ) measures the cost of assigning count c p and c q to two neighboring patch, and is normally referred to as the dis- continuity cost. For the problem of smoothing the adjacent patches count, D p (c p ) and V (c p − c q ) can take the form of the following functions: D p (c p ) = λ min((I(p) − c p ) 2 , DATA K) . . . (3) V (c p − c q ) = min((c p − c q ) 2 , DISC K) . . . (4) where λ is a weight of the energy items, I(p) is the ground truth count of the patch p, DATA K and DISC K are the truncating item of D p (c p ) and V (c p − c q ), respectively.
Can anyone explain the above part in detail and give me a detailed insight on how should I implement this part of the project?