6533b873fe1ef96bd12d4a9a

RESEARCH PRODUCT

Logo detection in images using HOG and SIFT

Jans GlagolevsKārlis Freivalds

subject

Artificial neural networkbusiness.industryComputer scienceHistogramFeature extractionComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONScale-invariant feature transformLogoPattern recognitionArtificial intelligencebusinessRotation (mathematics)Object detection

description

In this paper we present a study of logo detection in images from a media agency. We compare two most widely used methods — HOG and SIFT on a challenging dataset of images arising from a printed press and news portals. Despite common opinion that SIFT method is superior, our results show that HOG method performs significantly better on our dataset. We augment the HOG method with image resizing and rotation to improve its performance even more. We found out that by using such approach it is possible to obtain good results with increased recall and reasonably decreased precision.

yearjournalcountryeditionlanguage
2017-11-012017 5th IEEE Workshop on Advances in Information, Electronic and Electrical Engineering (AIEEE)
https://doi.org/10.1109/aieee.2017.8270535