Jianling Wang 903143548

1. Add additional, complementary features - Add gist descriptors

For this task, I generate gist descriptor of each image and append it to the normalized sift descriptor. All the parameters setting keep the same.The gist feature descriptor refers to http://people.csail.mit.edu/torralba/code/spatialenvelope/ . Adding the gist features can boost up the classification accuracy to 70.8%, which is the best performance I gain. So i show the confusion matrix in Fig 1. To test the combination of sift and gist feature, you need to change the set up to be(The table of classifier results will be shown in the latter part of this report)

%code for this part is in get_bags_of_gists
FEATURE = 'sift and gist';

2. Experiment with many different vocabulary sizes and report performance

For this task, I do experiment on vocabulary sizes of 10, 20, 50, 100, 200, 400. And I use the sift descriptors and all-SVM for classification. The corresponding accuracy values are shown in Fig 2. We find that the accuracy tends to converge after the size of vocabulary become larger. Since lager vocal_size will lead to longer processing time, it is important to select an appropriate vocal_size considering the tradeoff bewteen accuracy and effeciency.

Fig 2. Accuracy gained by bag of SIFT + 1 vs all linear SVM with different vocabulary sizes.

3. Train the SVM with chi-sqr

For this task, I train the SVM using chi-sqr kernel. Following the instruction provided in http://www.vlfeat.org/overview/svm.html, I first construct a chi-sqr dataset based for the training data. Then I feed the new dataset to vl_svmtrain. The code is provided below

Confusion matrix and the table of classifier results

%training for the SVM with chi-sqr
hom.kernel = 'KChi2';
hom.order = 2;
dataset = vl_svmdataset(train_image_feats', 'homkermap', hom);
[W, B] = vl_svmtrain(dataset, train_label', LAMBDA);

%calculate score when the jth item in ith category
score=(ws{i}')*(vl_homkermap(test_image_feats(j,:)', 2))+bs{i};

Fig 3. Confusion Matrix gained with bag of sift and SVM trained with chi-sqr.

Best Result Showcase

%In proj4.m
FEATURE = 'sift and gist';
CLASSIFIER = 'support vector machine';
vocab_size = 200;

%In svm_classify.m
LAMBDA=0.0001;