This is an implementation of the saliency model of the visual attention. The implementation mostly follows Itti’s TPAMI1998, except the winner-take-all neural network. (will be added next time) See the paper below for the implementation details.
L. Itti, C. Koch, and E. Niebur, "A model of saliency-based visual attention for rapid scene analysis," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 20, no. 11, pp. 1254–1259, 1998.
To run the model, CMake & OpenCV (>2.0) is required to run this code. Since the OpenCV cannot be installed without CMake, all you need is a working OpenCV library.
Simple demonstration is written in demo subdirectory. Follow the steps below.
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Make the compile directory and generate Makefile using CMake. There are several options for shared/static libraries.
$ mkdir test $ cd test $ cmake .. -
Make, and run the demo application. Image (larger than 640x480) is required.
$ cd demo $ ./saliency_demo
The included files are:
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src/saliency.h, .cc: The saliency model implementation.
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src/normalizer.h, .cc: The normalization operator implementation. There are two types for normalization, one is Local Max based, and another is Iterative Method based. For the details, see the paper below.
L. Itti and C. Koch, "Comparison of feature combination strategies for saliency-based visual attention systems," Electronic Imaging'99, pp. 473–482, May 1999.
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src/scalespace.h, .cc: The scale-space implementation.
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src/imgdump.h, .cc: To export the internal images for executing the saliency map.
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demo/demo.cc: A simple demonstration.