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[125140] Rozdział: A New Approach to Enhance Learning Data Sets for Object Classificationw książce: Proceedings of 26th International Conference on Circuits, Systems, Communications and Computers (CSCC)ISBN: 978-1-6654-8186-1 Wydawca: IEEE Computer Society Conference Publishing Services (CPS) Opublikowano: Lipiec 2022 Seria wydawnicza: Circuits, Systems, Communications and Computers (CSCC), International Conference on Liczba stron: 8 Liczba arkuszy wydawniczych: 0.40 Autorzy / Redaktorzy / Twórcy Grupa MNiSW: Autorstwo rozdziału w monografii spoza listy wydawnictw 2019 Punkty MNiSW: 5 Pełny tekst DOI Keywords: feature extraction  image classification  image recognition  image representation  image retrieval  learning artificial intelligence  object recognition  pattern classification  query processing  trees mathematics  visual databases  |
In this paper, we propose a novel solution to the problem of insufficient training data in complex image classification tasks. The presented method efficiently recommends new learning samples with the use of contour-based descriptors and Scalable Vocabulary Tree (SVT) technique. The SVT is built from SIFT features and may be used for identification and localization of objects of interest in the analyzed query images. In conditions where the training data set is not large enough, the SVT's classification accuracy is satisfactory and comparable to that achieved by the dedicated CNN network. However, the SVT approach surpasses the classic convnet approaches due to its incremental learning capability. The classification certainty is further increased thanks to contour-based analysis using Point Distance Histogram (PDH) descriptor. At this stage, the PDH representation of the analyzed object is matched with the information stored in the pattern database. This database contains a set of 360 shape outlines for each object that are extracted from its 3D model. This leads to higher classification accuracy of the presented approach.