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Incremental Estimation of Visual Vocabulary Size for Image Retrieval

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 529))

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Abstract

The increasing amount of image databases over the last years has highlighted our need to represent an image collection efficiently and quickly. The majority of image retrieval and image clustering approaches has been based on the construction of a visual vocabulary in the so called Bag-of-Visual-words (BoV) model, analogous to the Bag-of-Words (BoW) model in the representation of a collection of text documents. A visual vocabulary (codebook) is constructed by clustering all available visual features in an image collection, using k-means or approximate k-means, requiring as input the number of visual words, i.e. the size of the visual vocabulary, which is hard to be tuned or directly estimated by the total amount of visual descriptors. In order to avoid tuning or guessing the number of visual words, we propose an incremental estimation of the optimal visual vocabulary size, based on the DBSCAN-Martingale, which has been introduced in the context of text clustering and is able to estimate the number of clusters efficiently, even for very noisy datasets. For a sample of images, our method estimates the potential number of very dense SIFT patterns for each image in the collection. The proposed approach is evaluated in an image retrieval and in an image clustering task, by means of Mean Average Precision and Normalized Mutual Information.

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Notes

  1. 1.

    https://www.r-project.org/.

  2. 2.

    https://github.com/MKLab-ITI/topic-detection/blob/master/DBSCAN_Martingale.r.

  3. 3.

    https://cran.r-project.org/web/packages/dbscan/index.html.

  4. 4.

    http://wang.ist.psu.edu/docs/related/.

  5. 5.

    http://www.vision.caltech.edu/Image_Datasets/Caltech101/.

  6. 6.

    http://pami.xmu.edu.cn/~wlzhao/lip-vireo.htm.

  7. 7.

    https://github.com/MKLab-ITI/topic-detection/blob/master/DBSCAN_Martingale.r.

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Acknowledgements

This work was supported by the project MULTISENSOR (FP7-610411), funded by the European Commission.

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Authors and Affiliations

  1. Centre for Research and Technology Hellas, Information Technologies Institute, 6th Km Charilaou-Thermi Road, 57001, Thermi-Thessaloniki, Greece

    Ilias Gialampoukidis, Stefanos Vrochidis & Ioannis Kompatsiaris

Authors
  1. Ilias Gialampoukidis
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  2. Stefanos Vrochidis
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  3. Ioannis Kompatsiaris
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Corresponding author

Correspondence to Ilias Gialampoukidis .

Editor information

Editors and Affiliations

  1. School of Computing and Communications, Lancaster University , Lancaster, United Kingdom

    Plamen Angelov

  2. Data Engineering Lab, Dept. of Informatics, Aristotle University of Thessaloniki , Thessaloniki, Greece

    Yannis Manolopoulos

  3. Lab of Forest Informatics (FiLAB), Democritus University of Thrace , Orestiada, Greece

    Lazaros Iliadis

  4. WPC Information Systems Faculty, Arizona State University , Tempe, Arizona, USA

    Asim Roy

  5. Electrical Engineering Dept, (ICA), Pontifical Catholic Univ of Rio de Janei , Rio de Janeiro, Rio de Janeiro, Brazil

    Marley Vellasco

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Gialampoukidis, I., Vrochidis, S., Kompatsiaris, I. (2017). Incremental Estimation of Visual Vocabulary Size for Image Retrieval. In: Angelov, P., Manolopoulos, Y., Iliadis, L., Roy, A., Vellasco, M. (eds) Advances in Big Data. INNS 2016. Advances in Intelligent Systems and Computing, vol 529. Springer, Cham. https://doi.org/10.1007/978-3-319-47898-2_4

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  • DOI: https://doi.org/10.1007/978-3-319-47898-2_4

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  • Publisher Name: Springer, Cham

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