Homework answers / question archive / Assignment 2 CPS584 - Advanced Intelligent Systems and Deep Learning Requirements In this assignment, you will solve practical and interesting problems

Assignment 2 CPS584 - Advanced Intelligent Systems and Deep Learning Requirements In this assignment, you will solve practical and interesting problems

Computer Science

Share With

Assignment 2
CPS584 - Advanced Intelligent Systems and Deep Learning
Requirements
In this assignment, you will solve practical and interesting problems. By completing the project, you will gain valuable hands-on experience in the design, implementation and evaluation of classification algorithms. The details are listed as below.
You are provided with the “DogCat.zip” file which contains images of two classes: Dog and Cat. For each class, 25 training images and 15 testing images are given.
1. Extract the following features separately for each training and testing image:
a. Haar-like features (30-dimension as in Assignment 1). You should resize the image to the size of 128 x 128 (not 48 x 28 as in Assignment 1) before extracting Haar-like features.
b. Deep Learned Features at fc7 layer of AlexNet (4096-dimension). You should resize the image to the size of 227x227 before extracting Deep Learned Features. You may refer to Lab5 for AlexNet.
2. Use additional 30 training images (as you already collected in Assignment 1) for both classes: Dog and Cat. Perform the K-nearest neighbor (KNN with K ? {1, 3, 5, 7, 9}) and Neural Networks (NN) on the testing images. Note that both classifiers (KNN and NN) are trained on the new training data – there are 25 (already provided) + 30 (newly collected) = 55 training images for each class. And please report the accuracy rate for each class (KNN-Haar, KNN-Deep Learned Features, NN-Haar, NN-Deep Learned Features).
3. Flip all training images in (3) to have 45 more training images for each class (as shown in the example below). Please perform the K-nearest neighbor with different Ks (K ? {1, 3, 5, 7, 9}) with the new training data. And please report the accuracy rate for each class (KNN-Haar, KNN-Deep Learned Features).
FLIP
2
4. Use the same training set in (3). Concatenate Haar-like features and Deep Learned Features for each training and testing image (30 + 4096 = 4126-dimension). Perform the K-nearest neighbor with different Ks (K ? {1, 3, 5, 7, 9}). And please report the accuracy rate for each class (KNN-Haar+Deep Learned Features).
5. Discuss the accuracy rates in (2), (3), and (4). For example:
a. Will more training data lead to a better performance?
b. Will different Ks have different results?
c. Will feature concatenation improve the performance?
d. Your own observations.
What to Submit
1. A well-documented MATLAB program that implements the aforementioned problem in the Assignment 2. You must submit your program source code and the newly collected training data set.
2. A well-written, concise project report. It should include: (a) title and names of group members; (b) the analysis of each problem; (c) the issues during the implementation; (d) the solutions to overcome the issues in (c); (e) the contribution of each individual member; and (f) the powerpoint slides (maximum 20 slides) used in the Assignment presentation.
For each group, you must submit the files above in a single zipped folder. Your group will be required to do a face-to-face evaluation for the grading.