Toward Machine Learning on Granulated Data – a Case of Compact Autoencoder-based Representations of Satellite Images

Przyborowski, Mateusz; Tajmajer, Tomasz; Grad, Lukasz; Janusz, Andrzej; Biczyk, Piotr; Ślęzak, Dominik

doi:10.1109/bigdata.2018.8622562

Cited by 9 publications

(6 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to scale the system, a more granular approach of data analysis could be introduced, e.g. person tracking could be done at the crowd level initially, but at a single-person level when more details are needed [33], [34], [35].…”

Section: Discussionmentioning

confidence: 99%

Integrated Human Tracking Based on Video and Smartphone Signal Processing within the Arahub System

Ludziejewski

Grad

Przebinda³

et al. 2020

Proceedings of the 2020 Federated Conference on Computer Science and Information Systems

Self Cite

View full text Add to dashboard Cite

Embedded platforms with GPU acceleration, designed for performing machine learning on the edge, enabled the creation of inexpensive and pervasive computer vision systems. Smartphones are nowadays widely used for profiling and tracking in marketing, based on WiFi data or beacon-based positioning systems. We present the Arahub system, which aims at integrating world of computer vision systems with smartphone tracking for delivering data useful in interactive applications, such as interactive advertisements. In this paper we present the architecture of the Arahub system and provide insight about its particular elements. Our preliminary results, obtained from reallife test environments and scenarios, show that the Arahub system is able to accurately assign smartphones to their bearers, based on visual and WiFi/Bluetooth positioning data. We show the commercial value of such system and its potential applications.

show abstract

Section: Discussionmentioning

confidence: 99%

Integrated Human Tracking Based on Video and Smartphone Signal Processing within the Arahub System

Ludziejewski

Grad

Przebinda³

et al. 2020

Proceedings of the 2020 Federated Conference on Computer Science and Information Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…From a conceptual perspective, it corresponds to the aforementioned studies on aggregated / granulated / summarized information systems [63], [66]. This topic has also interesting relationships with some branches of approximate computing [107] and compressed image recognition [108].…”

Section: B Rough Set Contest At Pp-rai 2022mentioning

confidence: 99%

Rough Sets Turn 40: From Information Systems to Intelligent Systems

Skowron¹,

Ślęzak²

2022

Annals of Computer Science and Information Systems

View full text Add to dashboard Cite

The theory of rough sets was founded by Zdzisław Pawlak as a framework for data and knowledge exploration. His seminal paper titled "Rough Sets" was published in 1982, in International Journal of Computer and Information Sciences. One of the key aspects that lets us use rough sets in practical scenarios is the notion of information system, which comes from even earlier Professor Pawlak's works. Information systems are the means for data and knowledge representation. They constitute the input to rough set mechanisms aimed at computing approximations of concepts and deriving compacted, interpretable decision models. In particular, the fundamental notion of the indiscernibility relation is defined on the basis of a given information system. Accordingly, we discuss to what extent information systems can serve as the basis for intelligent systems. We claim that in many cases it is not enough to treat a data set -represented as an information system -as a purely abstract object with no linkage to the data origins. Oppositely, we should give ourselves a technical possibility to construct information systems dynamically, taking into account interaction with physical environments where the data comes from. With this respect, we refer to the notions of interactive granular computing and we generally consider together the paradigms of rough sets, information systems, and information granulation.

show abstract

“…Gaussian mixture models with Wasserstein distance, relies only on applied preprocessing. While autoencoder representations can be summaries themselves [4], given the large volume of the data, our method is far more practical.…”

Section: Stl-10 Datasetmentioning

confidence: 99%

Schema matching using Gaussian mixture models with Wasserstein distance

Przyborowski¹,

Pabiś²,

Janusz³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Gaussian mixture models find their place as a powerful tool, mostly in the clustering problem, but with proper preparation also in feature extraction, pattern recognition, image segmentation and in general machine learning. When faced with the problem of schema matching, different mixture models computed on different pieces of data can maintain crucial information about the structure of the dataset. In order to measure or compare results from mixture models, the Wasserstein distance can be very useful, however it is not easy to calculate for mixture distributions. In this paper we derive one of possible approximations for the Wasserstein distance between Gaussian mixture models and reduce it to linear problem. Furthermore, application examples concerning real world data are shown.

show abstract

Toward Machine Learning on Granulated Data – a Case of Compact Autoencoder-based Representations of Satellite Images

Cited by 9 publications

References 6 publications

Integrated Human Tracking Based on Video and Smartphone Signal Processing within the Arahub System

Integrated Human Tracking Based on Video and Smartphone Signal Processing within the Arahub System

Rough Sets Turn 40: From Information Systems to Intelligent Systems

Schema matching using Gaussian mixture models with Wasserstein distance

Contact Info

Product

Resources

About