Impact of Artificial Dataset Enlargement on Performance of Deformable Part Models

Yilmaz B., AMASYALI M. F., Balcilar M., USLU E., YAVUZ S.

24th Signal Processing and Communication Application Conference (SIU), Zonguldak, Türkiye, 16 - 19 Mayıs 2016, ss.193-196 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/siu.2016.7495710
  • Basıldığı Şehir: Zonguldak
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.193-196
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

There is a remarkable body of work for increasing the performance of Deformable Part Models (DPM), which is one of the most popular algorithms that are being used for object detection from digital images. The contribution that has been made to the object detection performance of the DPM algorithm via usage of larger datasets that has been created via production of artificial images from original images has been examined in this study. Various artificial dataset enlargement techniques that require no additional effort of labeling or data gathering have been compared on INRIA dataset and positive impact of artificial dataset enlargement has been observed. An increase on object detection performance has been noted both on the original INRIA dataset and its subsets.