Meaning of equation for CNN probabilities
I agree the equation might not be clear, but you can decompose it into something like the following:


First, the term  $\operatorname{argmax}_k p^i (y=k|\mathbf{x})$ tells you which label has the higher probability from model $i$ given the input object $\mathbf{x}$.
Then, this "iterates" over all models in the Committee, computing for each the label that is most likely. 
Finally finding which label is the most common one (that $\operatorname{argmax}_j$) at the end.


Also, it helps to think about it in pseudo-code

def get_label(CNNs, x):
    labels = [0, 0, 0, 0, 0]  # each position refers to that last $j$
    for pCNNi in CNNs:
        predictions = pCNNi(x)
        label_i = predictions.index(max(predictions))  # this is the argmax_k
        labels[label_i] += 1
    return labels.index(max(labels))  # this is the argmax_j