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A topological framework for training latent variable models
KTH, Skolan för bioteknologi (BIO), Genteknologi.
KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. (Computer Vision and Active Perception (CVAP) Lab)
KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
2014 (Engelska)Ingår i: Proceedings - International Conference on Pattern Recognition, 2014, s. 2471-2476Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

We discuss the properties of a class of latent variable models that assumes each labeled sample is associated with a set of different features, with no prior knowledge of which feature is the most relevant feature to be used. Deformable-Part Models (DPM) can be seen as good examples of such models. These models are usually considered to be expensive to train and very sensitive to the initialization. In this paper, we focus on the learning of such models by introducing a topological framework and show how it is possible to both reduce the learning complexity and produce more robust decision boundaries. We will also argue how our framework can be used for producing robust decision boundaries without exploiting the dataset bias or relying on accurate annotations. To experimentally evaluate our method and compare with previously published frameworks, we focus on the problem of image classification with object localization. In this problem, the correct location of the objects is unknown, during both training and testing stages, and is considered as a latent variable. ©

Ort, förlag, år, upplaga, sidor
2014. s. 2471-2476
Nyckelord [en]
Pattern recognition, Topology, Deformable part models, Latent variable models, Learning complexity, Object localization, Prior knowledge, Relevant features, Robust decisions, Training and testing, Image classification
Nationell ämneskategori
Elektroteknik och elektronik
Identifikatorer
URN: urn:nbn:se:kth:diva-167941DOI: 10.1109/ICPR.2014.427ISI: 000359818002099Scopus ID: 2-s2.0-84919941135ISBN: 9781479952083 (tryckt)OAI: oai:DiVA.org:kth-167941DiVA, id: diva2:817407
Konferens
22nd International Conference on Pattern Recognition, ICPR 2014, 24 August 2014 through 28 August 2014
Anmärkning

QC 20150605

Tillgänglig från: 2015-06-05 Skapad: 2015-05-22 Senast uppdaterad: 2015-09-14Bibliografiskt granskad

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Av författaren/redaktören
Afkham, Heydar MaboudiEk, Carl HenrikCarlsson, Stefan
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GenteknologiDatorseende och robotik, CVAP
Elektroteknik och elektronik

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