Feature Extraction by CNN and Classification by SVM

Introduction

　In the previous page, I performed the scene recognition using the Convolutional Neural Network (CNN) that the library Caffe provides. In this page, for the same dataset the same CNN is used for extracting feature vectors and the classification is accomplished by means of the Support Vector Machine (SVM) in the library LIBLINEAR. The recognition accuracy reaches about 95% .

Feature Extraction

　The following code is used to extract feature vectors from the CNN and to convert them into the input format that LIBLINEAR requires. The format of training and testing data files for LIBLINEAR is the same as that for LIBSVM: Each line contains a label and a feature vector. I extracted feature vectors from the layer "fc7." Their dimension is 4096. The contents of the file "total_list_15_in_local_machine.txt" described in the above code are as follows: Each line consists of a file path, a label, and a phase ("train"/"valid"/"test"). In this work, two phases "train" and "valid" are merged into one phase "train." After executing the above python code, I got two files "libsvm_train_inputs.txt" and "libsvm_test_inputs.txt" which are input files for LIBLINEAR. The number of training images are 7560 and the number of testing images 1220.

Execution of SVM

　The following command is run to train a SVM. Then, this command is run to predict the categories. The recognition accuracy is about as much as that of the CNN.