SBM: Sketch Based Interfaces and Modeling

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Browsing SBM: Sketch Based Interfaces and Modeling by Subject "based image retrieval"

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    Sketch Based Skirt Image Retrieval
    (ACM, 2014) Kondo, Shin-ichiro; Toyoura, Masahiro; Mao, Xiaoyang; Metin Sezgin
    Although many online shops allow users to search for clothes by categories or keywords, it is usually impossible to specify the details of the design. This paper presents a new technology for retrieving skirt images based on sketches. We first conducted a user study to investigate the typical features illustrated in a sketch. Then algorithms have been developed for automatically extracting those features from both the skirt images and the sketches. A prototype system has been implemented to retrieve and present the best matched skirts in real time when a user interactively sketches her imagined skirt on the canvas.
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    Sketch Based Skirt Image Retrieval
    (ACM, 2014) Kondo, Shin-ichiro; Toyoura, Masahiro; Mao, Xiaoyang; Metin Sezgin
    Although many online shops allow users to search for clothes by categories or keywords, it is usually impossible to specify the details of the design. This paper presents a new technology for retrieving skirt images based on sketches. We first conducted a user study to investigate the typical features illustrated in a sketch. Then algorithms have been developed for automatically extracting those features from both the skirt images and the sketches. A prototype system has been implemented to retrieve and present the best matched skirts in real time when a user interactively sketches her imagined skirt on the canvas.
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    SmartSketcher: Sketch-based Image Retrieval with Dynamic Semantic Reranking
    (Association for Computing Machinery, Inc (ACM), 2017) Portenier, Tiziano; Hu, Qiyang; Favaro, Paolo; Zwicker, Matthias; Holger Winnemoeller and Lyn Bartram
    We present a sketch-based image retrieval system, designed to answer arbitrary queries that may go beyond searching for predefined object or scene categories. While sketching is fast and intuitive to formulate visual queries, pure sketch-based image retrieval often returns many outliers because it lacks a semantic understanding of the query. Our key idea is to combine sketch-based queries with interactive, semantic re-ranking of query results. We leverage progress in deep learning and use a feature representation learned for image classification for re-ranking. This allows us to cluster semantically similar images, re-rank based on the clusters, and present more meaningful query results to the user. We report on two large-scale benchmarks and demonstrate that our re-ranking approach leads to significant improvements over the state of the art. Finally, a user study designed to evaluate a practical use case confirms the benefits of our approach.