An effective content-based image retrieval technique for image visuals representation based on the bag-of-visual-words model

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Abstract

For the last three decades, content-based image retrieval (CBIR) has been an active research area, representing a viable solution for retrieving similar images from an image repository. In this article, we propose a novel CBIR technique based on the visual words fusion of speeded-up robust features (SURF) and fast retina keypoint (FREAK) feature descriptors. SURF is a sparse descriptor whereas FREAK is a dense descriptor. Moreover, SURF is a scale and rotation-invariant descriptor that performs better in the case of repeatability, distinctiveness, and robustness. It is robust to noise, detection errors, geometric, and photometric deformations. It also performs better at low illumination within an image as compared to the FREAK descriptor. In contrast, FREAK is a retina-inspired speedy descriptor that performs better for classification-based problems as compared to the SURF descriptor. Experimental results show that the proposed technique based on the visual words fusion of SURF-FREAK descriptors combines the features of both descriptors and resolves the aforementioned issues. The qualitative and quantitative analysis performed on three image collections, namely Corel-1000, Corel-1500, and Caltech-256, shows that proposed technique based on visual words fusion significantly improved the performance of the CBIR as compared to the feature fusion of both descriptors and state-of-the-art image retrieval techniques.

Figures

  • Fig 1. (a) Semantic gap-Corel images of two different semantic categories (i.e. “Mountains” and “Beach”) with close visual appearance; (b) Two sample images of different shapes with close visual and semantic appearance (images used in the figure are similar but not identical to the original images used in the study due to copyright issue, and is therefore for illustrative purposes only).
  • Fig 2. Methodology of the BoVW based image representation for CBIR.
  • Fig 3. Block diagram of the proposed technique based on visual words fusion (images used in the figure are similar but not identical to the original images used in the study due to copyright issue, and is therefore for illustrative purposes only).
  • Table 1. Performance comparison and statistical analysis of different sizes of the dictionary and features percentages of the image on the Corel-1000 image collection (bold values indicate best performance).
  • Fig 4. Sample of images from different semantic categories of the Corel-1000 and Corel-1500 image collections (images used in the figure are similar but not identical to the original images used in the study due to copyright issue, and is therefore for illustrative purposes only).
  • Table 2. Performance analysis using adaptive or weighted feature fusion of SURF-FREAK descriptors on the Corel-1000 image collection (bold values indicate best performance).
  • Fig 5. Performance comparisons of standalone SURF, standalone FREAK, and features fusion of SURF and FREAK descriptors techniques on different sizes of the dictionary for the Corel-1000 image collection.
  • Table 3. Experimental details of the reported image collections for the proposed technique.

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CITATION STYLE

APA

Jabeen, S., Mehmood, Z., Mahmood, T., Saba, T., Rehman, A., & Mahmood, M. T. (2018). An effective content-based image retrieval technique for image visuals representation based on the bag-of-visual-words model. PLoS ONE, 13(4). https://doi.org/10.1371/journal.pone.0194526

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