TY  - JOUR
AU  - Hugo Jair Escalante
AU  - Victor Ponce
AU  - Sergio Escalera
AU  - Xavier Baro
AU  - Alicia Morales-Reyes
AU  - Jose Martinez-Carranza
ED  - Springer
PY  - 2017//
TI  - Evolving weighting schemes for the Bag of Visual Words
T2  - Neural Computing and Applications
JO  - Neural Computing and Applications
SP  - 925–939
VL  - 28
IS  - 5
KW  - Bag of Visual Words
KW  - Bag of features
KW  - Genetic programming
KW  - Term-weighting schemes
KW  - Computer vision
N2  - The Bag of Visual Words (BoVW) is an established representation in computer vision. Taking inspiration from text mining, this representation has provedto be very effective in many domains. However, in most cases, standard term-weighting schemes are adopted (e.g.,term-frequency or TF-IDF). It remains open the question of whether alternative weighting schemes could boost theperformance of methods based on BoVW. More importantly, it is unknown whether it is possible to automatically learn and determine effective weighting schemes fromscratch. This paper brings some light into both of these unknowns. On the one hand, we report an evaluation of the most common weighting schemes used in text mining, but rarely used in computer vision tasks. Besides, we propose an evolutionary algorithm capable of automatically learning weighting schemes for computer vision problems. We report empirical results of an extensive study in several computer vision problems. Results show the usefulness of the proposed method.
L1  - http://refbase.cvc.uab.es/files/EPE2016.pdf
UR  - http://dx.doi.org/10.1007/s00521-016-2223-x
N1  - HUPBA;MV; no menciona
ID  - Hugo Jair Escalante2017
ER  -