Fill This Form To Receive Instant Help

Help in Homework
trustpilot ratings
google ratings


Homework answers / question archive / Requirements  In this assignment, you will solve practical and interesting problems

Requirements  In this assignment, you will solve practical and interesting problems

Computer Science

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.zir file which contains images of two classes: Dog and Cat. For each class, 25 training images and 15 testing images are given. 
I. You need to re,size (imresize function in MATLAB) the image to the size 48 x 48. Then, you write MATLAB code to extract the Haar-like features for each training and testing image. The details are as follows: a. You compute the integral image (Lecture "Handcrafted Features", slides 16- 20). 
Original image 
111  ,1 1, 1 
Integral Image 

Stores Pixel suns of Rect(from ton-left corner to this point) Need 4 comer values 
b. By using the integral image, you compute thc Haar-like features with 30 boxes below. 

c. You then extract flaar-like features for both training and testing images. Each training/testing image has a 30-dim Bur-like feature. 2. You train 2 classifiers: K-ncarcst neighbor (KNN) and Neural Nctworks (NN) on thc Haar-like features of training images. Thcn, you test thc trained models on the Haar-like fcaturcs of thc testing images. Please report the accuracy ratc for cach class. You arc requested to try different K values: I, 3, 5, and 7. 3. Manually collect additional 30 training images for both classes: Dog and Cat. Note that those images must not be Identical to any already given training/tcsting image. You may need to usc any tool to resin and crop the newly collected images to the size 48 x 48. 4. Run 2 classifiers: K-ncarcst ncighbor and Neural Networics on thc testing images. Note that both classifiers (KNN and NN) are trained on the new trainina data — there are 25 (already provided) + 30 (newly collected). 55 training images for cach class. Again, 
you are requested to try different K. values: I, 3, 5, and 7. Please report the accuracy rate for each class 5. Discuss thc accuracy r.s in (2) and (4). Which one is better? Will more training data lead to a better performance? 
What to Submit 
I. A well-documented MATLAB program that implements the aforementioned problem in the Assignment I. You must submit your program sourcc codc 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) issues during the implementation; (d) the solutions to overcome the issues in (c); (e) the contribution of each individual member; and (f) thc powerpoint slides (maximum 20 slides) uscd in the Assignment presentation. For cach 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. One group member will submit file on behalf of the group. 
Important: Your submission will be thoroughly checked. If any plagiarism (from I.., former students, or anywhere clse) is found in this assignment, an F will bc assigned to coursc grade and an academic dishonesty report will be given. 

Option 1

Low Cost Option
Download this past answer in few clicks

23.99 USD

PURCHASE SOLUTION

Already member?


Option 2

Custom new solution created by our subject matter experts

GET A QUOTE