U-CATCH: Using Color ATtribute of image patCHes in binary descriptors
This method improves feature matching in computer vision by making binary descriptors color-sensitive, which is incremental as it builds on existing binary descriptor frameworks.
The paper tackled the problem of incorporating color information into binary feature descriptors, achieving more than 100% matching improvement compared to non-color binary descriptors for hard-to-match cases.
In this study, we propose a simple yet very effective method for extracting color information through binary feature description framework. Our method expands the dimension of binary comparisons into RGB and YCbCr spaces, showing more than 100% matching improve ment compared to non-color binary descriptors for a wide range of hard-to-match cases. The proposed method is general and can be applied to any binary descriptor to make it color sensitive. It is faster than classical binary descriptors for RGB sampling due to the abandonment of grayscale conversion and has almost identical complexity (insignificant compared to smoothing operation) for YCbCr sampling.