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Image Dating, a Case Study to Evaluate the Inter-Battery Topic Model
KTH, School of Computer Science and Communication (CSC).
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The Inter-Battery Topic Model (IBTM) is an extension of the well known Latent Dirichlet Allocation (LDA) topic model. It gives a factorized representation of multimodal (in this case two views) data, which better separates variation in observed data that is present in both views from variation that is present only in one of the separate views. This thesis is an evaluation and application study of this model with the aim of showing how it can be used in the very difficult classification task of dating grayscale face portraits from a dataset collected from highschool yearbooks. This task has very high intra-class variation and low inter-class variation which calls for techniques to extract the necessary information. An online-trained model is also implemented and evaluated as well as a simplification of the model more suited for this data specifically.

The results show improved performance over LDA showing that the factorizing property of IBTM has a positive effect on performance for this type of classification task.

Abstract [sv]

Inter-Battery Topic Model (IBTM) är en vidareutveckling av den välkända Latent Dirichlet Allocation (LDA) topic-modellen. Den ger en faktoriserad representation av multimodal data som bättre separerar variation i datat som finns i båda datavyer från den som finns i de enskilda datavyerna. Det här examensarbetet är en evaluering och applikationsstudie av modellen, med mål att visa hur den kan användas i den mycket svåra klassificeringsuppgiften att datera svartvita bilder från ett dataset skapat från amerikanska highschool-årsboksfoton. Denna klassificeringsuppgift har väldigt hög inom-klass variation samt väldigt låg mellan-klass variation vilket kräver bättre sätt att extrahera den nödvändiga information för bra klassificering. En online-tränad variant av modellen implementeras och evalueras också, samt en modellvariant som är mer anpassad för just denna typ av data.

Resultaten visar bättre prestanda än LDA vilket visar att den faktoriserade representationen från IBTM har en positiv effekt på prestanda in en klassificeringsuppgift av den här typen.

Place, publisher, year, edition, pages
2016. , 51 p.
Keyword [en]
topic model, image dating
National Category
Computer Science
URN: urn:nbn:se:kth:diva-190927OAI: diva2:953765
Subject / course
Computer Science
Educational program
Master of Science in Engineering - Computer Science and Technology
Available from: 2016-08-25 Created: 2016-08-18 Last updated: 2016-08-25Bibliographically approved

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Pertoft, John
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