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Segmentation data visualizing and clustering

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Abstract

Browsing, searching and retrieving images from large databases based on low level color or texture visual features have been widely studied in recent years but are also often limited in terms of usefulness. In this paper, we propose a new framework that allows users to effectively browse and search in large image database based on their segmentation-based descriptive content and, more precisely, based on the geometrical layout and shapes of the different objects detected and segmented in the scene. This descriptive information, provided at a higher level of abstraction, can be a significant and complementary information which helps the user to browse through the collection in an intuitive and efficient manner. In addition, we study and discuss various ways and tools for efficiently clustering or for retrieving a specific subset or class of images in terms of segmentation-based descriptive content which can also be used to efficiently summarize the content of the image database. Experiments conducted on the Berkeley Segmentation Datasets show that this new framework can be effective in supporting image browsing and retrieval tasks.

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Notes

  1. A region is a set of connected pixels belonging to the same class and a class, a set of pixels possessing similar textural characteristics.

  2. Our approach is tolerant to different types of image degradation (e.g., noise, blur, distortions) insofar as the segmentation method is able to give a segmentation map which is robust enough for these types of degradation.

  3. It is worth mentioning that we could also exploit all the set of ground truth segmentations for each image by computing an average VoI distance computed across all the existing ground truth segmentations.

  4. Source code (in C++ language) of our algorithm with the set of clustering and mapping results for each clustering strategy are publicly available at the following http address http://www.iro.umontreal.ca/~mignotte/ResearchMaterial/scvoi.html

  5. At this stage, it is important to recall that Google search image will not seek in a specific database (with 300 images as the BSDS300), but on the whole web image database and therefore it will have more choice to refine its search process which will be more accurate and efficient.

  6. This map has also been estimated on 3 other image databases, namely; 1) the Weizmann database (1 & 2 objects) (200 images), 2) the Microsoft Research Cambridge Object Recognition Image Database (MSRC) (591 images), 3) The Stanford Background Dataset (DAGS) (715 images). The references of these image databases and the obtained visualization maps are publicly available (with the source code of our algorithm) at the following http address: www.iro.umontreal.ca/~mignotte/ResearchMaterial/scvoi.html

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  1. Département d’Informatique et de Recherche Opérationnelle (DIRO), Université de Montréal, Faculté des Arts et des Sciences, Montréal, H3C 3J7 QC, Canada

    Ayman Khlif & Max Mignotte

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  1. Ayman Khlif
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  2. Max Mignotte
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Correspondence to Max Mignotte.

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Khlif, A., Mignotte, M. Segmentation data visualizing and clustering. Multimed Tools Appl 76, 1531–1552 (2017). https://doi.org/10.1007/s11042-015-3148-6

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  • DOI: https://doi.org/10.1007/s11042-015-3148-6

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