CODEWITHSUNDEEP
matlabinputcoordinatesneural-networkclassification
Es.T
Es.T

Reputation: 68

Coordinate normalization for NN input in matlab

I am trying to implement a classification NN in Matlab. My inputs are clusters of coordinates from an image. (Corresponding to delaunay triangulation vertexes) There are 3 clusters (results of the optics algorithm) in this format: enter image description here

( Not all clusters are of the same size.). Elements represent coordinates in euclidean 2d space . So (110,12) is a point in my image and the matrix depicted represents one cluster of points. Clustering was done on image edges. So coordinates refer to logical values (always 1s in this case) on the image matrix.(After edge detection there are 3 "dense" areas in an image, and these collections of pixels are used for classification). There are 6 target classes.

So, my question is how can I format them into single column vector inputs to use in a neural network? (There is a relevant answer here but I would like some elaboration if possible. ( I am probably too tired right now from 12 hours of trying stuff and dont get it 100% :D :( ) Remember, there are 3 different coordinate matrices for each picture, so my initial thought was, create an nn with 3 inputs (of different length). But how to serialize this?

Here's a cluster with its tags on in case it helps: enter image description here

Upvotes: 1

Views: 317

Answers (1)

Lord Henry Wotton
Lord Henry Wotton

Reputation: 1362

For you to train the classifier, you need a matrix X where each row will correspond to an image. If you want to use a coordinate representation, this means all images will have to be of the same size, say, M by N. So, the row of an image will have M times N elements (features) and the corresponding feature values will be the cluster assignments. Class vector y will be whatever labels you have, that is one of the six different classes you mentioned through the comments above. You should keep in mind that if you use a coordinate representation, X can get very high-dimensional, and unless you have a large number of images, chances are your classifier will perform very poorly. If you have few images, consider using fractions of pixels belonging to clusters that I suggested in one of the comments: this can give you a shorter feature description that is invariant to rotation and translation, and may yield better classification.

Upvotes: 1

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