VISHOD - Visual Dataset Scraping and Hybrid Outlier Detection - Moura, Flávio; Melo, Vitor; Alves, André; Pinto, Lyanh; Júnior, Walter; Santos, Adriano; Oliveira, Roberto; Cardoso, Diego; Seruffo, Marcos (Anais do Seminário Integrado de Software e Hardware (SEMISH), 2026)
Supervised learning depends on data quality, yet manual labeling is costly for resource-limited environments. This paper presents an automated pipeline for building and refining image datasets via web scraping and hybrid outlier detection. The method integrates automated collection, deep learning semantic extraction, consensus-driven anomaly detection, and statistical validation. As a case study, a high-quality dataset without manual annotation was produced for the classification of 2D geometric shapes for educational use in the Amazon. Results show a 25.6% increase in average PCA variance, a 5.6% reduction in centroid distance, and a 21.6% gain in the structural similarity index, evidencing greater diversity, cohesion, and visual homogeneity in the data.