FEATURE ENCODING MODELS FOR GEOGRAPHIC IMAGE RETRIEVAL AND CATEGORIZATION


Ozkan S., Ates T., Tola E., Soysal M., Esen E.

22nd IEEE Signal Processing and Communications Applications Conference (SIU), Trabzon, Türkiye, 23 - 25 Nisan 2014, ss.83-86 identifier

  • Basıldığı Şehir: Trabzon
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.83-86

Özet

In this work, we survey the perormance of various feature encoding models for geographic image retrieval task Recently introduced Vector-of-Locally-Aggregated Descriptors (VLAD) and its Product Quantization encoded binary version VLAD-PQ are compared with the widely used Bag-of-Word (BoW) model. Evaluation results are shown on a publicly available 21-class LULC dataset. With experiments, it is shown that VLAD outperforms classical BoW representation albeit with some increases in the computation time. Additionally, VLAD-PQ results in similar retrieval performance with VLAD but requiring no more computational or memory resources are observed