Video-based Clothing Retrieval
Date Issued
2013
Date
2013
Author(s)
Wang, Shih-Han
Abstract
Nowadays, clothing retrieval becomes a thriving demand for online clothing shopping websites. Beyond keyword-based clothing search, image-based clothing retrieval has generated interest in recent research papers. It promotes
more interesting clothing recommendation system and gives the possibility of improving identity or occupation recognition. In this paper, we present a brand-new video-based clothing retrieval system. We believe the system
gives another intuitive clothing recommendation interface in a smart home with such an application scenario: one can select an impressive shot where the character is wearing a fascinating clothing by a TV remote control, and learn the clothing style from the character. However, there still are major challenges in this topic, such as human pose estimation and complex background between online shopping datasets, which often cause inaccurate retrieval results. Our research focuses on two issues here. First, we propose a
human pose estimation mechanism with a video clip of frames for the refinement of inaccurate human pose. Second, we explore an automatic foreground segmentation method with "Grabcut" algorithm to tackle the complex background problem. In our experiments, we collect a few video clips and different kinds of online shopping datasets. The experimental results successfully demonstrate that our mechanism will improve the inaccurate pose estimation
and can tackle the complex background problem.
Subjects
前景切割
人體姿勢偵測
基於影像資訊
服裝檢索
Type
thesis
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