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International Journal of Multimedia Information Retrieval

Erschienen in:

01.11.2016 | Regular Paper

IR_URFS_VF: image recommendation with user relevance feedback session and visual features in vertical image search

verfasst von: D. Sejal, D. Abhishek, K. R. Venugopal, S. S. Iyengar, L. M. Patnaik

Erschienen in: International Journal of Multimedia Information Retrieval | Ausgabe 4/2016

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Abstract

In recent years, online shopping has grown exponentially and huge number of images are available online. Hence, it is necessary to recommend various product images to aid the user in effortless and efficient access to the desired products. In this paper, we present image recommendation framework with user relevance feedback session and visual features (IR_URFS_VF) to extract relevant images based on user inputs. User feedback is retrieved from image search history with clicked and un-clicked images. Image features are computed off-line and later used to find relevance between images. The relevance between images is determined by cosine similarity and are ranked based on clicked frequency and similarity score between images. Experiments results show that IR_URFS_VF outperforms CBIR method by providing more relevant ranked images to the user input query.

Vorheriger Artikel Improving content-based image retrieval with compact global and local multi-features

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Titel: IR_URFS_VF: image recommendation with user relevance feedback session and visual features in vertical image search
verfasst von: D. Sejal
D. Abhishek
K. R. Venugopal
S. S. Iyengar
L. M. Patnaik
Publikationsdatum: 01.11.2016
Verlag: Springer London
Erschienen in: International Journal of Multimedia Information Retrieval / Ausgabe 4/2016
Print ISSN: 2192-6611
Elektronische ISSN: 2192-662X
DOI: https://doi.org/10.1007/s13735-016-0111-x

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