Applying Large-Scale Image Retrieval to Near-Duplicate Image Detection
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Sähkötekniikan korkeakoulu |
Master's thesis
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Authors
Date
2016-10-27
Department
Major/Subject
Signaalinkäsittely
Mcode
S3013
Degree programme
Tietoliikennetekniikka
Language
en
Pages
70+9
Series
Abstract
Perceptual hashing outputs an image identifier that can be used for detecting images similar to the original image also known as near-duplicate images. ThingLink is a commercial image annotation service using perceptual hashing for placing annotations from the original annotated image to near-duplicate images. A customer is reporting 20-30% of near-duplicate images missing annotations. The system is working as expected calling for improved near-duplicate image detection (NDID) methodology. We apply Local Features and large-scale image retrieval to near- duplicate image detection. We use a Bag-of-Visual-Words-based image retrieval system for near-duplicate detection by assuming the original image always has the highest score of the images returned by Bag-Of-Visual-Words query. The query always returns the best matching image regardless of how good the match. We employ a cutoff score and classify all queries returning images with scores below the cutoff as no duplicate found. We show the Local Features and large- scale image retrieval system is better than the perceptual hash-based systems by generating seven different types of near-duplicate image sets from original images in two datasets. The originals form the image database. In addition we use a set of predicted images not in the database to determine how well the systems classify queries as no duplicate found. We show the optimal cutoff score to be the maximum score returned while querying predicted negative images for a given dataset. For matching near-duplicates the perceptual hashing schemes use the Hamming Distance, the number of bits by which hashes differ. We find an optimal Hamming Distances for both hashes. Despite tuning, we demonstrate Local Features and large-scale image retrieval to be the superior system for both datasets and all seven types of near-duplicate images used in near-duplicate image detection simulations.Hajautusfunktio on tapa luoda kuvatunniste identifioimaan kuvia, joissa voi esiintyä pieniä poikkeamia alkuperäiseen kuvaan nähden. ThingLink on kaupallinen kuvien annotaatiopalvelu, joka käyttää hajautusfunktioita palvellakseen alkuperäisen kuvan annotaatiot myös kuvaduplikaatteihin. 20-30% asiakkaan kuvaduplikaateista eivät tunnistu oikein. Tarvitaan parempi metodi kuvien duplikaattitunnistukseen. Sovellamme paikallisia piirteitä ”Bag-of-Visual-Words”-haun kanssa kuvien duplikaattitunnistukseen ja demonstroimme simulaatiolla metodin olevan parempi kuin käytössä olevat hajatusfunkiopohjaiset duplikaattitunnistimet.Description
Supervisor
Kannala, JuhoThesis advisor
Jalkanen, JanneKeywords
large-scale image retrieval, near-duplicate image detection, bag-of-visual-words, local features, perceptual hashing, binary classification