Search by image : clothing segmentation and retrieval

Our study aims to develop a product retrieval system for clothing items. More specifically, we formulate our task as a cross-domain content based image retrieval problem. Product search using traditional keyword matching can be inefficient and time- consuming due to the large semantic gap between l...

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Bibliographic Details
Main Author: Shi, Ganyu
Other Authors: Lin Guosheng
Format: Final Year Project (FYP)
Language:English
Published: 2019
Subjects:
Online Access:http://hdl.handle.net/10356/76912
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author Shi, Ganyu
author2 Lin Guosheng
author_facet Lin Guosheng
Shi, Ganyu
author_sort Shi, Ganyu
collection NTU
description Our study aims to develop a product retrieval system for clothing items. More specifically, we formulate our task as a cross-domain content based image retrieval problem. Product search using traditional keyword matching can be inefficient and time- consuming due to the large semantic gap between low-level visual features such as shape and colors, and high-level intents of customers. Our system aims to bridge the gap between shoppers and retailers by offering a street-to-shop image retrieval system that supports searching by images. We divide our system into two main stages: 1) Segmentation: We segment the query image into individual clothing classes, and use them as regions of interest for subsequent retrieval. 2) Retrieval: For each of the clothing item, we use image retrieval techniques to fetch visually similar products. We show that, by training effective CNN models, our system achieves fast and accurate clothing retrieval result, and is able to handle challenging scenarios such as diverse viewpoints, occlusion, and complex background.
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spelling ntu-10356/769122023-03-03T20:46:31Z Search by image : clothing segmentation and retrieval Shi, Ganyu Lin Guosheng School of Computer Science and Engineering DRNTU::Engineering::Computer science and engineering Our study aims to develop a product retrieval system for clothing items. More specifically, we formulate our task as a cross-domain content based image retrieval problem. Product search using traditional keyword matching can be inefficient and time- consuming due to the large semantic gap between low-level visual features such as shape and colors, and high-level intents of customers. Our system aims to bridge the gap between shoppers and retailers by offering a street-to-shop image retrieval system that supports searching by images. We divide our system into two main stages: 1) Segmentation: We segment the query image into individual clothing classes, and use them as regions of interest for subsequent retrieval. 2) Retrieval: For each of the clothing item, we use image retrieval techniques to fetch visually similar products. We show that, by training effective CNN models, our system achieves fast and accurate clothing retrieval result, and is able to handle challenging scenarios such as diverse viewpoints, occlusion, and complex background. Bachelor of Engineering (Computer Science) 2019-04-23T13:26:29Z 2019-04-23T13:26:29Z 2019 Final Year Project (FYP) http://hdl.handle.net/10356/76912 en Nanyang Technological University 42 p. application/pdf
spellingShingle DRNTU::Engineering::Computer science and engineering
Shi, Ganyu
Search by image : clothing segmentation and retrieval
title Search by image : clothing segmentation and retrieval
title_full Search by image : clothing segmentation and retrieval
title_fullStr Search by image : clothing segmentation and retrieval
title_full_unstemmed Search by image : clothing segmentation and retrieval
title_short Search by image : clothing segmentation and retrieval
title_sort search by image clothing segmentation and retrieval
topic DRNTU::Engineering::Computer science and engineering
url http://hdl.handle.net/10356/76912
work_keys_str_mv AT shiganyu searchbyimageclothingsegmentationandretrieval