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Image region re-weighting via multiple instance learning

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Abstract

In region-based approaches to content-based image retrieval each image is segmented into a set of regions and similarity between images is assessed by computing similarity between pairs of regions. A key factor in similarity measures that consider all pairs of regions to obtain an overall image-to-image similarity is the weighting of regions. The weight that is assigned to each region for determining similarity is usually based on heuristics that are often inconsistent with human perception of similarity. In this paper, we propose an approach that uses relevance feedback in conjunction with multiple instance learning to obtain more informed estimates of region weights. A comparative study is then carried out with alternative region re-weighting methods.

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Authors and Affiliations

  1. Department of Mathematics, Statistics, Computer Science, St. Francis Xavier University, Antigonish, NS, B2G 2W5, Canada

    Iker Gondra & Tao Xu

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  1. Iker Gondra
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  2. Tao Xu
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Correspondence to Iker Gondra.

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Gondra, I., Xu, T. Image region re-weighting via multiple instance learning. SIViP 4, 409–417 (2010). https://doi.org/10.1007/s11760-009-0128-1

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  • DOI: https://doi.org/10.1007/s11760-009-0128-1

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