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Visual Siamese Clustering for Cosmetic Product Recommendation

Lookup NU author(s): Dr Chris Holder, Professor Boguslaw ObaraORCiD


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© 2019, Springer Nature Switzerland AG. We investigate the problem of a visual similarity-based recommender system, where cosmetic products are recommended based on the preferences of people who share similarity of visual features. In this work we train a Siamese convolutional neural network, using our own dataset of cropped eye regions from images of 91 female subjects, such that it learns to output feature vectors that place images of the same subject close together in high-dimensional space. We evaluate the trained network based on its ability to correctly identify existing subjects from unseen images, and then assess its capability to find visually similar matches amongst the existing subjects when an image of a new subject is input.

Publication metadata

Author(s): Holder CJ, Obara B, Ricketts S

Editor(s): Gustavo Carneiro, Shaodi You

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 14th Asian Conference on Computer Vision (ACCV 2018 Workshops)

Year of Conference: 2019

Pages: 510-522

Online publication date: 19/06/2019

Acceptance date: 02/04/2018

ISSN: 0302-9743

Publisher: Springer Verlag


DOI: 10.1007/978-3-030-21074-8_40

Library holdings: Search Newcastle University Library for this item

Series Title: Lecture Notes in Computer Science

ISBN: 9783030210731