I am having five types of objects (flower, building, face, pair of shoes and a car) in my object recognition and i need to classify these. Identifying through edges in this type of data set seems to be a valid and distinguishable approach. So i have used canny edge detector(read it somewhere as a good approach) to find the edges in the image. Now i want to use this edged image to classify my objects. But since i am new to image recognition, i really couldn't figure how should i be selecting the features from this edged image for classification.
Will it be a good approach to use all the pixels in the image(after canny edge detector) as features to any ANN classifier, but i think that would give a lot of redundant features (as most of the image is black except the edges) and there might be a possibility to reduce these. Is there any algorithm to select appropriate features from the image formed after canny detector?
Second possibility could be to use any feature matching algorithm that could calculate the distance of the pixels between training and test data set(both edged) and predict the result with minimum distance. This is my approach but not sure about any existing algorithm. So needed some help on this.
Also i tried considering CNN(since they intrinsically use the edged approach) but these seem to be really computationally expensive.