APPAREL RECOMMENDATION WITH TRANSFER LEARNING AND LOCALITY SENSITIVE HASHING

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dc.contributor.advisorCho, Hyuk
dc.contributor.committeeMemberLiu, Qingzhong
dc.contributor.committeeMemberAn, Min Kyung
dc.contributor.committeeMemberIslam, ABM R
dc.creatorGundogan, Kubra
dc.creator.orcid0000-0002-5861-430X
dc.date.accessioned2023-01-09T14:32:24Z
dc.date.available2023-01-09T14:32:24Z
dc.date.created2022-12
dc.date.issued2022-12-01T06:00:00.000Z
dc.date.submittedDecember 2022
dc.date.updated2023-01-09T14:32:25Z
dc.description.abstractThe textile and apparel industries have now grown a lot and there is a variety of clothing that is constantly renewed or changed throughout the world. Given the abundance of selection options available, we developed a system that takes an image a user provides and then offers a recommendation which matches the user’s query image. This study developed a cloth recommendation system, which employs transfer learning with a pre-trained deep learning model (VGG16) followed by locality sensitive hashing with random projection. The dataset was originated by the H&M company and was exhibited in a competition via Kaggle. This dataset contains 105K image data in total by addressing 130 different categories in five (5) main groups. Among a total of 7,000 of the Ladieswear group, occupying about 37.7% in the dataset, a balanced dataset was obtained by splitting the 7,000 images into seven (7) clothing groups. These groups are labeled dress, trousers, sweater, blouse, skirt, t-shirt, and vest top. Specifically, we extracted embedded features of the image using transfer learning and achieved a fast recommendation using locality sensitive hashing. We demonstrated the effectiveness of the proposed recommendation system by comparing the average cosine similarity of top 6 recommendations before and after locality sensitive hash. Furthermore, we qualitatively visualized the quality of the recommendation.
dc.format.mimetypeapplication/pdf
dc.identifier.uri
dc.identifier.urihttps://hdl.handle.net/20.500.11875/3822
dc.language.isoEnglish
dc.subjectComputer Science
dc.titleAPPAREL RECOMMENDATION WITH TRANSFER LEARNING AND LOCALITY SENSITIVE HASHING
dc.typeThesis
dc.type.materialtext
thesis.degree.collegeCollege of Science and Engineering Technology
thesis.degree.departmentComputer Science
thesis.degree.disciplineComputing and Data Science
thesis.degree.grantorSam Houston State University
thesis.degree.nameMaster of Science

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