This document discusses how to perform similar image search using OpenCV. It explains that images are first preprocessed through grayscale conversion, blurring, and histogram equalization. Interesting image features are then extracted using techniques like Hessian, FAST, and Laplacian of Gaussian detectors. These features are matched between the input image and a feature database using fast lookup and FLANN. OpenCV is recommended for its implementation of computer vision algorithms and hardware acceleration support, though it can have issues with compilation and unstable APIs.

1. Similar image search
with OpenCV
Jernej Virag / @jernejv

2. How does it work?
● Preprocess images
● Find interesting parts of images ( features)
● Find matching features of input image in feature
database

3. Preprocessing images
● Grayscale conversion
● Bluring
● Histogram equalization

4. Feature extraction
● Hessian feature detector
● FAST
● Laplacian of Gaussian
● etc.
● Better algorithms
● SIFT (patented)
● SURF (patented)

5. Feature matching
● Fast lookup of nearest vectors
● We'll use FLANN

6. OpenCV
● Great:
● Tons of pre-implemented algorithms
● REALLY fast
● Hardware acceleration support
● Less great:
● Annoying compilation
● Broken packages
● Unstable API
● Large

7. OpenCV
● Should compile your own!
● Add TBB and libeigen for performance
● Don't forget python headers!
● Use “cmake-gui” to check results of pre-compile
configuration

8. CODE TIME!

9. ?
@jernejv / mavrik@kiberpipa.org

10. ?
@jernejv / mavrik@kiberpipa.org