@article{MTMT:34231292,
	title = {Multi-camera trajectory matching based on hierarchical clustering and constraints},
	url = {https://m2.mtmt.hu/api/publication/34231292},
	author = {Szűcs, Gábor and Borsodi, R. and Papp, Dávid},
	doi = {10.1007/s11042-023-17397-0},
	journal-iso = {MULTIMED TOOLS APPL},
	journal = {MULTIMEDIA TOOLS AND APPLICATIONS: AN INTERNATIONAL JOURNAL},
	volume = {83},
	unique-id = {34231292},
	issn = {1380-7501},
	year = {2024},
	eissn = {1573-7721},
	pages = {44879-44902},
	orcid-numbers = {Szűcs, Gábor/0000-0002-5781-1088}
}

@article{MTMT:32630750,
	title = {Determining Hybrid Re-id Features of Vehicles in Videos for Transport Analysis},
	url = {https://m2.mtmt.hu/api/publication/32630750},
	author = {Papp, Dávid and Borsodi, Regő},
	doi = {10.36244/ICJ.2022.1.3},
	journal-iso = {INFOCOMM J},
	journal = {INFOCOMMUNICATIONS JOURNAL},
	volume = {14},
	unique-id = {32630750},
	issn = {2061-2079},
	abstract = {The research topic presented in this paper belongs to computer vision problems in the transport application area, where the statistical data of the results give the input for the transport analysis. Although object tracking in a controlled environment could be performed with good results in general, accurate and detailed annotation of vehicles is a common problem in traffic analysis. Such annotation includes static and dynamic attributes of numerous vehicles. Most recent object trackers employ CNNs to compute the so-called re-identification features of the bounding boxes. In this paper we introduce hybrid re-identification features, which combine latent, static, and dynamic attributes to improve tracking. Furthermore, we propose a lightweight solution that could be integgrated in a real-time multi-camera tracking system.},
	year = {2022},
	eissn = {2061-2125},
	pages = {17-23}
}

@article{MTMT:32236541,
	title = {Zero Initialised Unsupervised Active Learning by Optimally Balanced Entropy-Based Sampling for Imbalanced Problems},
	url = {https://m2.mtmt.hu/api/publication/32236541},
	author = {Szűcs, Gábor and Papp, Dávid},
	doi = {10.1080/0952813X.2021.1924871},
	journal-iso = {J EXP THEOR ARTIF IN},
	journal = {JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE},
	volume = {34},
	unique-id = {32236541},
	issn = {0952-813X},
	abstract = {Given the challenge of gathering labelled training data for machine learning tasks, active learning has become popular. This paper focuses on the beginning of unsupervised active learning, where there are no labelled data at all. The aim of this zero initialised unsupervised active learning is to select the most informative examples – even from an imbalanced dataset – to be labelled manually. Our solution with proposed selection strategy, called Optimally Balanced Entropy-Based Sampling (OBEBS) reaches a balanced training set at each step to avoid imbalanced problems. Two theorems of the optimal solution for selection strategy are also presented and proved in the paper. At the beginning of the active learning, there is not enough information for supervised machine learning method, thus our selection strategy is based on unsupervised learning (clustering). The cluster membership likelihoods of the items are essential for the algorithm to connect the clusters and the classes, i.e., to find assignment between them. For the best assignment, the Hungarian algorithm is used, and single, multi, and adaptive assignment variants of OBEBS method are developed. Based on generated and real images datasets of handwritten digits, the experimental results show that our method surpasses the state-of-the-art methods.},
	year = {2022},
	eissn = {1362-3079},
	pages = {781-814},
	orcid-numbers = {Szűcs, Gábor/0000-0002-5781-1088}
}

@mastersthesis{MTMT:32745833,
	title = {Felügyelet nélküli és kiegyenlítettség-vezérelt aktív tanulásos képosztályozás},
	url = {https://m2.mtmt.hu/api/publication/32745833},
	author = {Papp, Dávid},
	publisher = {Budapest University of Technology and Economics},
	unique-id = {32745833},
	year = {2021}
}

@{MTMT:32546390,
	title = {Digitális lábnyomok és hatásuk a pénzügyi rendszerre},
	url = {https://m2.mtmt.hu/api/publication/32546390},
	author = {Gulyás, Gábor György and Gönczy, László and Kocsis, Imre and Magyar, Gábor and Papp, Dávid and Szűcs, Gábor},
	booktitle = {A digitális transzformáció technológiai kérdései},
	unique-id = {32546390},
	year = {2021},
	pages = {19-171},
	orcid-numbers = {Szűcs, Gábor/0000-0002-5781-1088}
}

@article{MTMT:32534202,
	title = {Zero Initialized Active Learning with Spectral Clustering using Hungarian Method},
	url = {https://m2.mtmt.hu/api/publication/32534202},
	author = {Papp, Dávid},
	doi = {10.14232/ACTACYB.288006},
	journal-iso = {ACTA CYBERN-SZEGED},
	journal = {ACTA CYBERNETICA},
	volume = {25},
	unique-id = {32534202},
	issn = {0324-721X},
	year = {2021},
	eissn = {2676-993X},
	pages = {401-419}
}

@CONFERENCE{MTMT:32508257,
	title = {Incorporation of object detection models and location data into snake species classification},
	url = {https://m2.mtmt.hu/api/publication/32508257},
	author = {Regő, Borsodi and Papp, Dávid},
	booktitle = {Proceedings of the Working Notes of CLEF 2021},
	unique-id = {32508257},
	abstract = {Photo-based automatic snake species identification could assist in clinical management of snakebites. LifeCLEF announced the SnakeCLEF 2021 challenge, which aimed attention on this task and provided location metadata for most snake images. This paper describes the participation of the BME-TMIT team in this challenge. In order to reduce clutter and drop the unnecessary background, we employed the state-of-the-art EfficientDet object detector, which was fine-tuned on manually annotated images. Detected snakes were then classified by EfficientNet weighted with the likelihood of location information. Based on the official evaluation of SnakeCLEF 2021, our solution achieved an F1 country score of 0.903, which placed our team at rank 1 position in the challenge. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).},
	year = {2021},
	pages = {1499-1511}
}

@CONFERENCE{MTMT:31656568,
	title = {Spectral Clustering based Active Zero-shot Learning},
	url = {https://m2.mtmt.hu/api/publication/31656568},
	author = {Papp, Dávid},
	booktitle = {The 12th Conference of PhD Students in Computer Science},
	unique-id = {31656568},
	year = {2020},
	pages = {54-57}
}

@article{MTMT:31615387,
	title = {Graph construction with condition-based weights for spectral clustering of hierarchical datasets},
	url = {https://m2.mtmt.hu/api/publication/31615387},
	author = {Papp, Dávid and Knoll, Zsolt and Szűcs, Gábor},
	doi = {10.36244/ICJ.2020.2.5},
	journal-iso = {INFOCOMM J},
	journal = {INFOCOMMUNICATIONS JOURNAL},
	volume = {12},
	unique-id = {31615387},
	issn = {2061-2079},
	abstract = {Most of the unsupervised machine learning algorithms focus on clustering the data based on similarity metrics, while ignoring other attributes, or perhaps other type of connections between the data points. In case of hierarchical datasets, groups of points (point-sets) can be defined according to the hierarchy system. Our goal was to develop such spectral clustering approach that preserves the structure of the dataset throughout the clustering procedure. The main contribution of this paper is a set of conditions for weighted graph construction used in spectral clustering. Following the requirements – given by the set of conditions – ensures that the hierarchical formation of the dataset remains unchanged, and therefore the clustering of data points imply the clustering of point-sets as well. The proposed spectral clustering algorithm was tested on three datasets, the results were compared to baseline methods and it can be concluded the algorithm with the proposed conditions always preserves the hierarchy structure.},
	year = {2020},
	eissn = {2061-2125},
	pages = {34-40},
	orcid-numbers = {Szűcs, Gábor/0000-0002-5781-1088}
}

@inproceedings{MTMT:31014870,
	title = {Machine preparation for human labelling of hierarchical train sets by spectral clustering},
	url = {https://m2.mtmt.hu/api/publication/31014870},
	author = {Papp, Dávid and Szűcs, Gábor and Knoll, Zsolt},
	booktitle = {10th IEEE International Conference on Cognitive Infocommunications, (CogInfoCom 2019)},
	doi = {10.1109/CogInfoCom47531.2019.9089906},
	unique-id = {31014870},
	year = {2019},
	pages = {157-162},
	orcid-numbers = {Szűcs, Gábor/0000-0002-5781-1088}
}