Original title: Porovnání signaturových a sémantických podobnostních modelů
Translated title: Comparison of signature-based and semantic similarity models
Authors: Kovalčík, Gregor ; Lokoč, Jakub (advisor) ; Mráz, František (referee)
Document type: Bachelor's theses
Year: 2017
Language: eng
Abstract: Content-based image retrieval and similarity search has been investigated for several decades with many different approaches proposed. This thesis fo- cuses on a comparison of two orthogonal similarity models on two different im- age retrieval tasks. More specifically, traditional image representation models based on feature signatures are compared with models based on state-of-the-art deep convolutional neural networks. Query-by-example benchmarking and tar- get browsing tasks were selected for the comparison. In a thorough experimental evaluation, we confirm that models based on deep convolutional neural networks outperform the traditional models. However, in the target browsing scenario, we show that the traditional models could still represent an effective option. We have also implemented a feature signature extractor into the OpenCV library in order to make the source codes available for the image retrieval and computer vision community. 1
Keywords: CNN descriptors; color signatures; image retrieval; similarity search; barevné signatury; CNN deskriptory; podobnostní vyhledávání; vyhledávání v obrázcích

Institution: Charles University Faculties (theses) (web)
Document availability information: Available in the Charles University Digital Repository.
Original record: http://hdl.handle.net/20.500.11956/90464

Permalink: http://www.nusl.cz/ntk/nusl-365061


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Universities and colleges > Public universities > Charles University > Charles University Faculties (theses)
Academic theses (ETDs) > Bachelor's theses