Upload
vivien-matthews
View
215
Download
1
Tags:
Embed Size (px)
Citation preview
FINAL STEPS
Calculate feature matrix of each image, isolating the object first
Concatenate matrices Apply PCA function to preprocess data Multiply each individual feature matrix by result Concatenate output into 1 matrix Apply GMM function and obtain mean,
covariance, and prior mode probabilities Apply Fisher Vector to each individual result to
obtain vectors that are the same size Use those fisher vectors for 11 SVM’s (one for
each color)
COMPLETE STEPS Using Ebay Data (omitting binary images) Use all Google Data (from 30 to 100 images per color) Increase cluster size in GMM from 10 to 128
LINEAR SVM
First tried it with libsvm code MATLAB Function: svmtrain (Training, Group) Training: Data to be processed (transpose
matrix) Group: Specifies +1 or -1 data Use SVM for each color (black, blue, brown,
green, grey, orange, pink, purple, red, white, yellow)
Changed to fitcsvm(X,Y)
CLASSIFY DATA
MATLAB Function: svmclassify(SVMStruct,Sample)
Use SVMStruct from svmtrain (from each color)
Sample: Concatenated Ebay Fisher Vectors Changed to predict(SVMModel, X) SVMModel from fitcsvm
SVM CLASSIFY OUTPUTColumn vector with the same number of rows
as Sample. Each entry (row) in Group represents the class of the corresponding row
of Sample.
CALCULATE PRECISION
Calculate 12 highest scores for each color, using first column only
Determine if each score is a correct match by checking indices
Calculate each color’s precision