Instructions
Answer sheets must be submitted on Canvas. Hard copies will not be accepted. Please submit your answer sheet containing the written answers in a file named:
FirstName_LastName_PS3.pdf.
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.
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.
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.
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.
Short answer problems [10 points]
ECS 174: Intro to Computer Vision, Spring 2018 Problem Set 3
Instructor: Yong Jae Lee (yongjaelee@ucdavis.edu) TA: Chongruo Wu ( )
TA: Maheen Rashid ( ) TA: Yash Bhartia (yvbhartia@ucdavis.edu)
Due: Thursday, June 7th, 11:59 PM
crwu@ucdavis.edu
mhnrashid@ucdavis.edu
1. 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. What exactly does the value recorded in a single dimension of a SIFT keypoint descriptor signify?
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 and deep features here:
https://drive.google.com/open?id=10yk7tvDfmge9fEVm2XbwAmaIRL9R7clK
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
imname
numfeats
orients
positions
scales
deepFC7
Provided code
nx128 double 1×57 char 1×1 double nx1 double nx2 double nx1 double
1×4096 double
// SIFT vectors as rows
// name of image file that goes with this data // number of detected features
// orientations of the patches
// positions of the patch centers
// scales of the patches
// AlexNet FC7 feature for entire image
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
- Visualizing the vocabulary [20 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 (e.g., say 25 patches per word displayed). See provided helper function getPatchFromSIFTParameters.m. Explain what you see. Name your script visualize_vocabulary.m. Please submit your visual words in a file called kMeans.mat. This file should contain a matrix of size kx128 called kMeans.
- Full frame queries [20 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
- Region queries [20 pts]: Select your favorite query regions from within 4 frames (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
5. Full frame queries, Part 2: comparing SIFT bag-of-words with Deep Features [10 pts]: Use frames friends_0000004503.jpeg and friends_0000000394.jpeg to serve as queries. For each query display: the query frame and (1) its M=10 most similar frames based on the normalized scalar product between their bag of words histograms and (2) its M=10 most similar frames based on the normalized scalar product between their AlexNet fully-connected layer 7 activation features (stored as variable deepFC7). (The AlexNet was pre-trained on the 1000-class ImageNet classification task, and we are using it to extract the layer-7 activation features for each image.) Explain the differences between the retrieval results obtained using the SIFT bag-of-words features versus the pre-trained deep convolutional neural network features. Which does better? Why? Name your script compare_bow_and_deep.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
• •
• •
•
3
•
descriptors to build the vocabulary.
Map a raw SIFT descriptor to its visual word. The raw descriptor is assigned to the nearest
visualword. [seeprovideddist2.mcodeforfastdistancecomputations]
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 withinthatregion. [seeprovidedselectRegion.mcode]
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.