Similarity between images
Final project by
Uriya assayag
Roi kotlovski
Introduction
In this report, we will present an experiment that we conducted. The experiment was
to examine similarity between images by people, and compare the results to a
similarity by computing description.
Experiment process
There were 35 participants that had to choose the most similar image to a main image,
out of 3 possible images. This routine repeated 200 iterations.
In each iteration, 4 random images, from the images pool, were chosen and presented
to the participant. Note that the 4 presented images in the same iteration were all
different.
Images pool – 400 B&W images, in size of 256x384 pixels.
Data processing
Image descriptors are descriptions of the visual features of the contents in images,they
describe elementary characteristics such as the shape, color , texture or motion, among
others.
There are many image descriptors,which are used in order to measure the similarity
between images. We chose the GIST descriptor for this task.
The GIST main idea is to develop a low dimensional representation of the scene,
which does not require any form of segmentation. The GIST computes the description
with a consideration of set of perceptual dimensions (naturalness, openness,
roughness, expansion, ruggedness) that represent the dominant spatial structure of a
scene.The image is divided into a 4-by-4 grid for which orientation histograms are
extracted. (from 'Evalutation of GIST descriptors for web-scale image search')
Themethod that we usedformeasuring the similarity between two different images is
as follows:
Take the GIST representation of the two images and computeit's distance byEuclidean
norm.
According to our method, the chosen image (between the three) is the one with the
minimum distance from the main image.
We were interested in examining the resemblance between the selections made by the
participants and by ourmethod. In order to compare between them,we took the 35
scenarios which were introduced to the participants and ran the implemented method
on each one of them.
Finally, we took the algorithm output, and compared it to the participant's results.
Results
We had a total of 6500 scenarios. By comparing the results of the participants to our
method results, we got 37% matching results.
We'll show a couple of "good" selections and some "bad" ones from hypothetic
scenarios.
Example 1
The selection method, selected pic1.
Example 2
The selection method, selected pic3.
Example 3
The selection method, selected pic1.
Example 4
The selection method, selected pic3.
Example 5
The selection method, selected pic2.
Characteristic examples
Example 1
Example 2
Example 3
Conclusion
We can see, by the 3 last characteristic examples above, the big difficulty that can
emerge when we'll try to choose the most similar image. By that we conclude that the
selection by our method in comparison to the participants one, is almost identical to a
dice throw.