%0 Journal Article %T Evolving weighting schemes for the Bag of Visual Words %A Hugo Jair Escalante %A Victor Ponce %A Sergio Escalera %A Xavier Baro %A Alicia Morales-Reyes %A Jose Martinez-Carranza %E Springer %J Neural Computing and Applications %D 2017 %V 28 %N 5 %F Hugo Jair Escalante2017 %O HUPBA;MV; no menciona %O exported from refbase (http://refbase.cvc.uab.es/show.php?record=2743), last updated on Mon, 20 Jul 2020 11:49:04 +0200 %X 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. %K Bag of Visual Words %K Bag of features %K Genetic programming %K Term-weighting schemes %K Computer vision %U http://refbase.cvc.uab.es/files/EPE2016.pdf %U http://dx.doi.org/10.1007/s00521-016-2223-x %P 925–939