Kernel-based Efficient Subwindow Search software

Brief description

This is an implementation of subwindow search methods for object detection and localization that employs the SMAWK algorithm to support distance kernels for comparing feature histograms (rather than requiring the features be weighted): [1] ESS from Lampert and Blaschko (CVPR 2008) modified to handle distance kernels. [2] Alternating Row-Column Search (ARCS) (CVPR 2010) (formerly named A-ESS. Additionally, a couple of other ESS-like algorithms are provided: [1] Improved-ESS from An et al (CVPR 2009) modified to handle distance kernels. [2] Bentley's algorithm, also modified to handle distance kernels. Distance kernels provided are Chi-Squared and Weighted-Chi-Squared, although other histogram distance measures can easily be substituted in. The code will compile for Visual Studio 2005/2008 (a project is included in the .zip), and the source code should compile under Linux and probably under any platform supported by GCC.