@INPROCEEDINGS{8904931, 
author =  {R. {Aljuaidi} and J. {Su} and R. {Dahyot}}, 
booktitle =  {2019 30th Irish Signals and Systems Conference (ISSC)}, 
title =  {Mini-Batch VLAD for Visual Place Retrieval}, 
note = {Awarded Best Student Paper at ISSC 2019. Github: https://github.com/ReemTCD/Mini_Batch_VLAD},
year =  {2019}, 
volume =  {}, 
number =  {}, 
pages =  {1-6}, 
abstract = {This study investigates the visual place retrieval of an image query using a geotagged image dataset.
Vector of Locally Aggregated Descriptors (VLAD) is one of
the local features that can be used for image place recognition.
VLAD describes an image by the difference of its local feature
descriptors from an already computed codebook. Generally, a
visual codebook is generated from k-means clustering of the
descriptors. However, the dimensionality of visual features is
not trivial and the computational load of sample distances in
a large image dataset is challenging. In order to design an
accurate image retrieval method with affordable computation
expenses, we propose to use the mini-batch k-means clustering
to compute VLAD descriptor(MB-VLAD). The proposed MBVLAD technique shows advantage in retrieval accuracy in
comparison with the state of the art techniques.},
keywords =  {feature extraction;content-based image retrieval;image processing}, 
doi =  {10.1109/ISSC.2019.8904931}, 
url={https://mural.maynoothuniversity.ie/15129/1/RD_mini%20batch.pdf},
ISSN =  {2688-1446},
month =  {June}}

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