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Closing the Performance Gap between Siamese Networks for Dissimilarity Image Classification and Convolutional Neural Networks

Sensors (Basel, Switzerland), 2021-08, Vol.21 (17), p.5809 [Peer Reviewed Journal]

2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2021 by the authors. 2021 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s21175809 ;PMID: 34502700

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  • Title:
    Closing the Performance Gap between Siamese Networks for Dissimilarity Image Classification and Convolutional Neural Networks
  • Author: Nanni, Loris ; Minchio, Giovanni ; Brahnam, Sheryl ; Sarraggiotto, Davide ; Lumini, Alessandra
  • Subjects: Animals ; Classification ; Clustering ; Datasets ; discrete cosine transform ; ensemble of classifiers ; Image classification ; Image retrieval ; loss function ; Neural networks ; Neural Networks, Computer ; Prototypes ; Siamese networks ; Sum rules ; Support vector machines
  • Is Part Of: Sensors (Basel, Switzerland), 2021-08, Vol.21 (17), p.5809
  • Description: In this paper, we examine two strategies for boosting the performance of ensembles of Siamese networks (SNNs) for image classification using two loss functions (Triplet and Binary Cross Entropy) and two methods for building the dissimilarity spaces (FULLY and DEEPER). With FULLY, the distance between a pattern and a prototype is calculated by comparing two images using the fully connected layer of the Siamese network. With DEEPER, each pattern is described using a deeper layer combined with dimensionality reduction. The basic design of the SNNs takes advantage of supervised k-means clustering for building the dissimilarity spaces that train a set of support vector machines, which are then combined by sum rule for a final decision. The robustness and versatility of this approach are demonstrated on several cross-domain image data sets, including a portrait data set, two bioimage and two animal vocalization data sets. Results show that the strategies employed in this work to increase the performance of dissimilarity image classification using SNN are closing the gap with standalone CNNs. Moreover, when our best system is combined with an ensemble of CNNs, the resulting performance is superior to an ensemble of CNNs, demonstrating that our new strategy is extracting additional information.
  • Publisher: Switzerland: MDPI AG
  • Language: English
  • Identifier: ISSN: 1424-8220
    EISSN: 1424-8220
    DOI: 10.3390/s21175809
    PMID: 34502700
  • Source: GFMER Free Medical Journals
    MEDLINE
    PubMed Central
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
    DOAJ Directory of Open Access Journals
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