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Image Retrieval for Online Browsing in Large Image Collections

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNISA,volume 8199)

Abstract

Two new methods for large scale image retrieval are proposed, showing that the classical ranking of images based on similarity addresses only one of possible user requirements. The novel retrieval methods add zoom-in and zoom-out capabilities and answer the “What is this?” and “Where is this?” questions.

The functionality is obtained by modifying the scoring and ranking functions of a standard bag-of-words image retrieval pipeline. We show the importance of the DAAT scoring and query expansion for recall of zoomed images.

The proposed methods were tested on a standard large annotated image dataset together with images of Sagrada Familia and 100000 image confusers downloaded from Flickr. For completeness, we present in detail components of image retrieval pipelines in state-of-the-art systems. Finally, open problems related to zoom-in and zoom-out queries are discussed.

Keywords

  • Image Retrieval
  • Visual Word
  • Ranking Function
  • Query Image
  • Query Expansion

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Mikulik, A., Chum, O., Matas, J. (2013). Image Retrieval for Online Browsing in Large Image Collections. In: Brisaboa, N., Pedreira, O., Zezula, P. (eds) Similarity Search and Applications. SISAP 2013. Lecture Notes in Computer Science, vol 8199. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41062-8_2

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  • DOI: https://doi.org/10.1007/978-3-642-41062-8_2

  • Publisher Name: Springer, Berlin, Heidelberg

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