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

Abstract

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).