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A region proposal algorithm using texture similarity and perceptual grouping

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Abstract

Currently, the most prominent object recognition and image labeling techniques are based on the region proposal algorithms. One of the significant challenges of the region proposal algorithms is to achieve high Recall at high overlaps. This paper proposes a new region proposal algorithm using perceptual grouping to generate fitting regions to enhance the Recall at high overlaps. The proposed method comprises segmentation, region merging, based on texture descriptors, and similarity measurement. Furthermore, the algorithm introduces a hybrid approach to compute an efficient threshold. To fully assess the proposed algorithm, well-known metrics such as overlap and Recall are measured. Experimental results are reported on MSRC, VOC2007, VOC2012, and COCO 2017 datasets. The results are compared with segmentation algorithms, and several classical and deep learning-based region proposals. The evaluation results indicate a good improvement of the Recall at high overlaps, such as 0.8 and 0.9, with a reasonable number of regions.

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Data Availability Statement

All datasets that support the findings of this study are available from their generators. The generators are cited in the References of this article.

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Authors and Affiliations

  1. Razi Uiversity, Kermanshah, Iran

    Maryam Taghizadeh & Abdolah Chalechale

  2. Iowa state University, Ames, IA, USA

    Ali Jannesari

Authors
  1. Maryam Taghizadeh
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  2. Abdolah Chalechale
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  3. Ali Jannesari
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Correspondence to Abdolah Chalechale.

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Taghizadeh, M., Chalechale, A. & Jannesari, A. A region proposal algorithm using texture similarity and perceptual grouping. J Ambient Intell Human Comput 14, 271–288 (2023). https://doi.org/10.1007/s12652-021-03296-5

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