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RingFIR: A Large Volume Earring Dataset for Fashion Image Retrieval

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Computer Vision and Image Processing (CVIP 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1377))

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Abstract

Fashion image retrieval (FIR) is a challenging task, which involves similar item searching from a massive collection of fashion products based on a query image. FIR in different garments and shoes are popular in literature. More complex fashion products such as ornaments are getting less attention. Here, we introduce a new earring dataset, namely, RingFIR. The dataset is a collection of (\(\sim \)2.6K) high-quality images collected from major India based jewellery chains. The dataset is labelled in 46 classes in a structured manner. We have benchmarked the dataset using state-of-the-art image retrieval methods. We believe that the dataset is challenging and will attract computer vision researchers in the future. The dataset is available publicly (https://github.com/skarifahmed/RingFIR).

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Author information

Authors and Affiliations

  1. Global Institute of Science and Technology, Purba Medinipur, India

    Sk Maidul Islam

  2. Haldia Institute of Technology, Purba Medinipur, India

    Subhankar Joardar

  3. UiT The Arctic University of Norway, Tromsø, Norway

    Arif Ahmed Sekh

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  1. Sk Maidul Islam
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  2. Subhankar Joardar
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  3. Arif Ahmed Sekh
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Editor information

Editors and Affiliations

  1. Indian Institute of Information Technology Allahabad, Prayagraj, India

    Satish Kumar Singh

  2. Indian Institute of Technology Roorkee, Roorkee, India

    Partha Roy

  3. Indian Institute of Technology Roorkee, Roorkee, India

    Balasubramanian Raman

  4. Indian Institute of Information Technology Allahabad, Prayagraj, India

    P. Nagabhushan

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Islam, S.M., Joardar, S., Sekh, A.A. (2021). RingFIR: A Large Volume Earring Dataset for Fashion Image Retrieval. In: Singh, S.K., Roy, P., Raman, B., Nagabhushan, P. (eds) Computer Vision and Image Processing. CVIP 2020. Communications in Computer and Information Science, vol 1377. Springer, Singapore. https://doi.org/10.1007/978-981-16-1092-9_9

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  • DOI: https://doi.org/10.1007/978-981-16-1092-9_9

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