UCORM: Indexing Uncorrelated Metric Spaces for Concise Content-Based Retrieval of Medical Images

Zabot, Guilherme F.; Cazzolato, Mirela T.; Scabora, Lucas C.; Faiçal, Bruno S.; Traina, Agma J. M.; Traina, Caetano

doi:10.1109/cbms.2019.00070

Cited by 1 publication

(2 citation statements)

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“…Previous use of data. A small part of FeatSet has been employed in the previous studies [Zabot et al 2019a, Zabot et al 2019b]. In that works, the authors explored different visual features to validate a novel Multi-Metric Access Method, aiming at indexing complex objects based on images' visual characteristics and the correlation among the distance spaces.…”

Section: Introductionmentioning

confidence: 99%

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FeatSet: A Compilation of Visual Features Extracted from Public Image Datasets

Cazzolato

Scabora

Zabot

et al. 2021

Anais Do III Dataset Showcase Workshop (DSW 2021)

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In this paper, we present FeatSet, a compilation of visual features extracted from open image datasets reported in the literature. FeatSet has a collection of 11 visual features, consisting of color, texture, and shape representations of the images acquired from 13 datasets. We organized the available features in a standard collection, including the available metadata and labels, when available. We also provide a description of the domain of each dataset included in our collection, with visual analysis using Multidimensional Scaling (MDS) and Principal Components Analysis (PCA) methods. FeatSet is recommended for supervised and non-supervised learning, also widely supporting Content-Based Image Retrieval (CBIR) applications and complex data indexing using Metric Access Methods (MAMs).

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Section: Introductionmentioning

confidence: 99%

“…In this work, we present an extended and complete version of the data used in [Zabot et al 2019a, Zabot et al 2019b. FeatSet is a new dataset, composed of diverse visual features extracted from various public image datasets of different application scenarios.…”

Section: Introductionmentioning

confidence: 99%