In CNN (Convolutional Neural Network), does the combination of previous layer's filters make next layer's filters? I know that the first layer uses a low-level filter to see the edge information. As the layer gets deeper, it will represent high-level (abstract) information. Is it because the combinations of filters used in the previous layer are used as filters in the next layer? ("Does the combination of the previous layer's filters make the next layer's filters?) If so, are the combinations determined in advance?
I guess you are making mistake about the filters. After applying filters to the input of each layer, the output will be used as the input of the next layer. The first layer's filters try to find the edges in the image and their output show whether those edges exist in a specified position or not. Next layer filters try to find patterns in the outputs of the previous layer which shows the existence of edges. Due to the point that each filter is a window and specifies a receptive field on the input, it finds patterns in the input which are more abstract and more complicated than the previous layers' activations.