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\(S^{2}\)-LOR: Supervised Stream Learning for Object Recognition

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Pattern Recognition and Image Analysis (IbPRIA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14062))

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Abstract

In a stream learning scenario, where new data arrives at a slow pace, it is crucial to leverage new knowledge at the same rate without losing prior knowledge, and without assuming data stationarity. This scenario presents a significant challenge for incremental learning, particularly for tasks such as object recognition in video streams. In this paper, a novel approach is proposed that uses a set of weak classifiers that evolves into ensembles to enhance the generalization power of the system, as new video subsequences of the same instances are presented. We evaluate the efficiency of our approach and compare with state-of-the-art methods using a benchmark dataset. The code is available at https://github.com/vilaB/object_recognition.

C. D. Parga and G. Vilariño—These authors contributed equally to this work.

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Notes

  1. 1.

    https://vlomonaco.github.io/core50/.

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Acknowledgments

This work has received financial support from the Spanish government (project PID2020-119367RB-I00); from the Xunta de Galicia, Consellaría de Cultura, Educación e Ordenación Universitaria (accreditations 2019-2022 ED431G-2019/04 and ED431G 2019/01, and reference competitive groups 2021-2024 ED431C 2021/48 and ED431C 2021/30), and from the European Regional Development Fund (ERDF/FEDER).

Author information

Authors and Affiliations

  1. Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain

    César D. Parga & Xosé M. Pardo

  2. Universidade de Santiago de Compostela, Santiago de Compostela, Spain

    Gabriel Vilariño

  3. Universidade da Coruña, CITIC, Computer Architecture Group, A Coruña, Spain

    Carlos V. Regueiro

Authors
  1. César D. Parga
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  2. Gabriel Vilariño
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  3. Xosé M. Pardo
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  4. Carlos V. Regueiro
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Corresponding author

Correspondence to César D. Parga .

Editor information

Editors and Affiliations

  1. University of Alicante, Alicante, Spain

    Antonio Pertusa

  2. University of Alicante, Alicante, Spain

    Antonio Javier Gallego

  3. Universitat Politècnica de València, Valencia, Spain

    Joan Andreu Sánchez

  4. IPO Porto, Coimbra, Portugal

    Inês Domingues

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Parga, C.D., Vilariño, G., Pardo, X.M., Regueiro, C.V. (2023). \(S^{2}\)-LOR: Supervised Stream Learning for Object Recognition. In: Pertusa, A., Gallego, A.J., Sánchez, J.A., Domingues, I. (eds) Pattern Recognition and Image Analysis. IbPRIA 2023. Lecture Notes in Computer Science, vol 14062. Springer, Cham. https://doi.org/10.1007/978-3-031-36616-1_24

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  • DOI: https://doi.org/10.1007/978-3-031-36616-1_24

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  • Online ISBN: 978-3-031-36616-1

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