Automatic evaluation of iconic image retrieval based on colour, shape, and texture

Riku Togashi, Sumio Fujita, Tetsuya Sakai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Product image search is required to deal with large target image datasets which are frequently updated, and therefore it is not always practical to maintain exhaustive and up-to-date relevance assessments for tuning and evaluating the search engine. Moreover, in similar product image search where the query is also an image, it is difficult to identify the possible search intents behind it and thereby verbalise the relevance criteria for the assessors, especially if graded relevance assessments are required. In this study, we focus on similar product image search within a given product category (e.g., shoes), wherein each image is iconic (i.e., the image clearly shows what the product looks like and basically nothing else), and propose an initial approach to evaluating the task without relying on manual relevance assessments. More specifically, we build a simple probabilistic model that assumes that an image is generated from latent intents representing shape, texture, and colour, which enables us to estimate the relevance score of each image and thereby compute graded relevance measures for any image search engine result page. Through large-scale crowdsourcing experiments, we demonstrate that our proposed measures, InDCG (which is based on per-intent binary relevance) and D-InDCG (which is based on per-intent graded relevance), align reasonably well with human SERP preferences and with human image preferences. Hence, our automatic measures may be useful at least for rough tuning and evaluation of similar product image search.

Original languageEnglish
Title of host publicationICMR 2020 - Proceedings of the 2020 International Conference on Multimedia Retrieval
PublisherAssociation for Computing Machinery, Inc
Pages346-354
Number of pages9
ISBN (Electronic)9781450370875
DOIs
Publication statusPublished - 2020 Jun 8
Event10th ACM International Conference on Multimedia Retrieval, ICMR 2020 - Dublin, Ireland
Duration: 2020 Jun 82020 Jun 11

Publication series

NameICMR 2020 - Proceedings of the 2020 International Conference on Multimedia Retrieval

Conference

Conference10th ACM International Conference on Multimedia Retrieval, ICMR 2020
CountryIreland
CityDublin
Period20/6/820/6/11

Keywords

  • Evaluation
  • Graded relevance
  • Iconic images
  • Image search
  • Product search
  • Retrieval effectiveness

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

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  • Cite this

    Togashi, R., Fujita, S., & Sakai, T. (2020). Automatic evaluation of iconic image retrieval based on colour, shape, and texture. In ICMR 2020 - Proceedings of the 2020 International Conference on Multimedia Retrieval (pp. 346-354). (ICMR 2020 - Proceedings of the 2020 International Conference on Multimedia Retrieval). Association for Computing Machinery, Inc. https://doi.org/10.1145/3372278.3390741