Assignment 1 CS4186 Vision and Image

Assignment 1
CS4186 Vision and Image

instance search
You are given a collection of 5,000 images, 10 example query instances and 20 testing query instances (you can download from this link: google driver link). Each image contains one instance (object). Your task is to implement two methods for instance search. Specifically, given a query instance, a method needs to find the images that contain the same query instance from the image collection （5000 images） and then ranks them according to similarity or confidence. The 20 testing query images are used for evaluating the performance of your implementation. 
Marking scheme
    Report (no more than 5 pages): Write a report that briefly describes the two methods and compares their search performance for the 10 example query instances. The performance should be measured by mean average precision (MAP). The MAP calculation is provided. You should also show the results for some queries by listing the retrieved images.                                                 (30%)
    Method (50%): 

Algorithm: Implement any two methods (e.g., color histogram, LBP, SIFT, CNN) for instance search. You can employ the methods you learn in the class, or you could choose other methods in the literature. It is also encouraged to design the method by yourself. (40%)
Try to combine two methods (e.g., color histogram, LBP, SIFT, CNN), which may provide better performance than using only one method.         (10%)
At least one method should be implemented by yourself, instead of calling the ready-made function package.

    Benchmarking: For each of your methods, show the retrieval result for each of the 20 testing query instances (as detailed in the submission section). The mark will be allocated based on the retrieval performance of the method with better performance.                                                                                                                                (20%)
Deadline
The submission should be done before 09-Apr 2021, 11:59pm.
Penalty on late submission: 20% of marks will be deducted per day. No submission will be allowed after five days from the deadline. Do NOT copy code from the internet and do not borrow other people’s code. Remember that PLAGIARISM is a serious offense for which you may fail the class or even be expelled from the university.
NOTE
    In the provided zip file, the folders are organized as: images – 5,000 images for search; examples – 10 example query instances; Queries – 20 testing query instances.
    The instance of an image is given as bounding box. The bounding box is represented as (top left corner in x, top left corner in y, width, height) in the text file. For example, the file 01.txt specifies the bounding box of the query instance for image 01.jpg.
    The location of instance, given as a bounding box, is also provided for 2,000 images. 
    In the examples folder, you can find the program metric_map.py for measuring search performance in terms of MAP. The value of MAP will be high (at most 1.0) if the images containing the query instances are ranked at top.
    In the examples folder, the ground-truth (or answers) for the 10 examples queries can be found in the file rank_groundtruth.txt.
    You can use any distance or similarity function for measuring the similarity between two images or instances. Examples include:
Euclidean distance between a query Q=[q_1,q_2⋯q_n] and an image F=[f_1,f_2⋯f_n]:
Distance(Q,F)=√(∑_(i=1)^n▒〖(q_i-f_i)〗^2 )
            Cosine similarity between a query Q=[q_1,q_2⋯q_n] and an image F=[f_1,f_2⋯f_n]:
Similarity(Q,F)=(∑_(i=1)^n▒〖q_i f_i 〗)/(√(∑_(i=1)^n▒q_i^2 ) √(∑_(i=1)^n▒f_i^2 ))



Submission
Please zip the followings and submit to Canvas:
    Computer program: Python/Matlab/C++ recommended. If you want to use another program language, please send an email to our Teaching Assistant.
    Report
    Rank list: A text file (rankList.txt) showing the images in descending order of similarity:
Q1: 7 12 214 350 …
Q2: 301 501 1990 2 …
Q3: 288 345 389 1290 …
Q4: 248 293 1098 2000 …
Q5: 380 287 392 478 222 …

Remark: 20 rows only (for 20 testing query instances); Each row should list the names of the 5,000 images (named in number) in descending order of their similarities to a query.

Contact person
Please email Teaching Assistant Qiudan ZHANG (Email: qdzhang3-c@my.cityu.edu.hk) for technical questions.