FEATURE ENCODING MODELS FOR GEOGRAPHIC IMAGE RETRIEVAL AND CATEGORIZATION

Date

2014-04-25

Author

Ozkan, Savas
Ates, Tayfun
Tola, Engin
Soysal, Medeni
Esen, Ersin

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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

Subject Keywords

Conferences , Computational Modeling, Signal Processing , Computer Vision , Image Retrieval , Histograms , Pattern Recognition

URI

https://hdl.handle.net/11511/68110

Conference Name

22nd IEEE Signal Processing and Communications Applications Conference (SIU)

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Department of Computer Engineering, Conference / Seminar

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Citation Formats

S. Ozkan, T. Ates, E. Tola, M. Soysal, and E. Esen, “FEATURE ENCODING MODELS FOR GEOGRAPHIC IMAGE RETRIEVAL AND CATEGORIZATION,” Karadeniz Teknik Univ, Trabzon, TURKEY, 2014, p. 83, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/68110.