ECS 174: Intro to Computer Vision, Spring 2020
Problem Set 3

Instructor: Yong Jae Lee (yongjaelee@ucdavis.edu)
Instructor: Krishna Kumar Singh (krsingh@ucdavis.edu)
TA: Haotian Liu (lhtliu@ucdavis.edu)
TA: Utkarsh Ojha (uojha@ucdavis.edu)
TA: Yuheng Li (yhnli@ucdavis.edu)

Due: Monday, June 1st, 11:59 PM

Instructions
1. Answer sheets must be submitted on Canvas. Hard copies will not be accepted.

FirstName_LastName_PS3.pdf.

3. Please submit your code and input /output images in a zip file named:
FirstName_LastName_PS3.zip. Please do not create subdirectories within the main directory.

4. You may complete the assignment individually or with a partner (i.e., maximum group of 2
people). If you worked with a partner, provide the name of your partner. We will be using
MOSS to check instances of plagiarism/cheating.

5. For the implementation questions, make sure your code is documented, is bug-free, and works
out of the box. Please be sure to submit all main and helper functions. Be sure to not
include absolute paths. Points will be deducted if your code does not run out of the box.

6. If plots are required, you must include them in your answer sheet (pdf) and your code must
display them when run. Points will be deducted for not following this protocol.

1 Short answer problems [10 points]

1. What exactly does the value recorded in a single dimension of a SIFT keypoint descriptor signify?

2. When performing interest point detection with the Laplacian of Gaussian, how would results differ
if we were to (a) take any positions that are local maxima in scale-space, or (b) take any positions
whose filter response exceeds a threshold? Specifically, what is the impact on repeatability or
distinctiveness of the resulting interest points?

2 Programming: Video search with bag of visual words [90 points]

For this problem, you will implement a video search method to retrieve relevant frames from a video
based on the features in a query region selected from some frame. We are providing the image data and
some starter code for this assignment.

Provided data

You can access pre-computed SIFT features here:

The associated images are stored here:
https://ucdavis.box.com/s/ylxih5tgwja1azx78jkc0d5awcxla71m

Please note the data takes about 6 GB. Each .mat file in the provided SIFT data corresponds to a single
image, and contains the following variables, where n is the number of detected SIFT features in that image:

descriptors nx128 double // SIFT vectors as rows
imname 1x57 char // name of image file that goes with this data
numfeats 1x1 double // number of detected features
orients nx1 double // orientations of the patches
positions nx2 double // positions of the patch centers
scales nx1 double // scales of the patches

Provided code

The following are the provided code files. You are not required to use any of these functions, but you will
probably find them helpful. You can access the code here:
https://ucdavis.box.com/s/cll544a6gq4zaqgf6emn9uf3cq5gwy51

• loadDataExample.m: Run this first and make sure you understand the data format. It is a
script that shows a loop of data files, and how to access each SIFT descriptor. It also shows
how to use some of the other functions below.

• displaySIFTPatches.m: given SIFT descriptor info, it draws the patches on top of an image

• getPatchFromSIFTParameters.m: given SIFT descriptor info, it extracts the image patch
itself and returns as a single image

• selectRegion.m: given an image and list of feature positions, it allows a user to draw a polygon
showing a region of interest, and then returns the indices within the list of positions that fell
within the polygon.

• dist2.m: a fast implementation of computing pairwise distances between two matrices for
which each row is a data point

• kmeansML.m: a faster k-means implementation that takes the data points as columns
What to implement and discuss in the write-up

Write one script for each of the following (along with any helper functions you find useful), and in your pdf
writeup report on the results, explain, and show images where appropriate. Your code must access the frames
and the SIFT features from subfolders called ‘frames’ and ‘sift’, respectively, in your main working
directory.

1. Raw descriptor matching [20 pts]: Allow a user to select a region of interest (see provided
selectRegion.m) in one frame, and then match descriptors in that region to descriptors in
the second image based on Euclidean distance in SIFT space. Display the selected region of interest
in the first image (a polygon), and the matched features in the second image, something like the
below example. Use the two images and associated features in the provided file
twoFrameData.mat (in the zip file) to demonstrate. Note, no visual vocabulary should be
used for this one. Name your script raw_descriptor_matches.m

2. Visualizing the vocabulary [25 pts]: Build a visual vocabulary. Display example image patches
associated with two of the visual words. Choose two words that are distinct to illustrate what the
different words are capturing, and display enough patch examples so the word content is evident
(25 patches per word displayed). See provided helper function
getPatchFromSIFTParameters.m. Explain what you see. Name your script
This file should contain a matrix of size kx128 called kMeans.

3. Full frame queries [25 pts]: After testing your code for bag-of-words visual search, choose 3
different frames from the entire video dataset to serve as queries. Display each query frame and
its M=5 most similar frames (in rank order) based on the normalized scalar product between
their bag of words histograms. Explain the results. Name your script
full_frame_queries.m

4. Region queries [20 pts]: Select your favorite query regions from 3 frames of your choice (which
may be different than those used above) to demonstrate the retrieved frames when only a portion
of the SIFT descriptors are used to form a bag of words. Try to include example(s) where
the same object is found in the most similar M frames but amidst different objects or
backgrounds, and also include a failure case. Display each query region (marked in the frame
as a polygon) and its M=5 most similar frames. Explain the results, including possible reasons
for the failure cases. Name your script region_queries.m

Tips: overview of framework requirements

The basic framework will require these components:

• Compute nearest raw SIFT descriptors. Use the Euclidean distance between SIFT descriptors
to determine which are nearest among two images’ descriptors. That is, “match” features from
one image to the other, without quantizing to visual words.

• Form a visual vocabulary. Cluster a large, representative random sample of SIFT descriptors
from some portion of the frames using k-means. Let the k centers be the visual words. The
value of k is a free parameter; for this data something like k=1500 should work, but feel free
to play with this parameter [see Matlab’s kmeans function, or provided kmeansML.m code].
Note: you may run out of memory if you use all the provided SIFT descriptors to build the
vocabulary.

• Map a raw SIFT descriptor to its visual word. The raw descriptor is assigned to the nearest
visual word. [see provided dist2.m code for fast distance computations]

• Map an image’s features into its bag-of-words histogram. The histogram for image I j is a k-
dimensional vector: F (I j ) = [ freq1,j , freq2,j , … , freqk,j], where each entry freqi,j counts the
number of occurrences of the i-th visual word in that image, and k is the number of total words
in the vocabulary. In other words, a single image’s list of n SIFT descriptors yields a k-
dimensional bag of words histogram. [Matlab’s histc is a useful function]

• Compute similarity scores. Compare two bag-of-words histograms using the normalized
scalar product.

• Sort the similarity scores between a query histogram and the histograms associated with the
rest of the images in the video. Pull up the images associated with the M most similar examples.
[see Matlab’s sort function]

• Form a query from a region within a frame. Select a polygonal region interactively with the
mouse, and compute a bag of words histogram from only the SIFT descriptors that fall within
that region. [see provided selectRegion.m code]

• There may be some frames (e.g., all black) in which no features are detected and hence no
descriptors are available. If so, you will need to ignore those frames (e.g., using an if statement).

3 OPTIONAL: Extra credit (10 points)

• Stop list and tf-idf. Implement a stop list to ignore very common words, and apply tf-idf weighting
to the bags of words. Discuss and create an experiment to illustrate the impact on your results.

Email:51zuoyejun

@gmail.com