Open AccessFeatSet: A Compilation of Visual Features Extracted from Public Image Datasets
Author(s) -
Mirela T. Cazzolato,
Lucas C. Scabora,
Guilherme F. Zabot,
Marco Antônio Gutierrez,
Caetano Traina,
Agma J. M. Traina
Publication year - 2021
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/dsw.2021.17417
Subject(s) - computer science , metadata , search engine indexing , image retrieval , artificial intelligence , metric (unit) , information retrieval , pattern recognition (psychology) , principal component analysis , visualization , domain (mathematical analysis) , image (mathematics) , world wide web , mathematics , operations management , economics , mathematical analysis
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).