|M.Sc Student||Igor Kviatkovsky|
|Subject||Color Invariants for Person Re-Identification|
|Department||Department of Computer Science||Supervisors||Full Professor Heymann Michael|
|Full Professor Rivlin Ehud|
|Dr. Adam Amit|
|Full Thesis text|
We revisit the problem of specific object recognition using color distributions. In some applications - such as specific person identification - it is highly likely that the color distributions will be multimodal and hence contain a special structure.
Although the color distribution changes under different lighting conditions, some aspects of its structure turn out to be invariants. We refer to this structure as an intra-distribution structure, and show that it is invariant under a wide range of imaging
conditions while being discriminative enough to be practical. Our signature uses shape context descriptors to represent the intra-distribution structure. Assuming the widely used diagonal model, we validate that our signature is invariant under certain
illumination changes. Experimentally, we use color information as the only cue to obtain good recognition performance on publicly available databases covering both indoors and outdoors conditions. Combining our approach with the complementary covariance descriptor, we demonstrate results exceeding the state of the art performance on the challenging VIPeR database.