BoCS: Image retrieval using explicable methods

Autor(es)

Silva, Endi D. C. ; Traina, Agma J. M.

• Tipo de documento: Artigo Científico
• Fonte: 2023 36TH CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES, SIBGRAPI 2023; v. N/A, p. 6-pg., 2023-01-01.

Resumo

In recent years, image generation has been growing at a very fast pace, demanding specific systems for managing large image datasets. For example, we can mention the content-based image retrieval (CBIR) systems. Usually, they use a representation of feature vectors based on the images' visual content to store/retrieve them and to perform demanded queries. Currently, neural networks perform the task of generating image representations with great mastery. However, these networks usually create methods that are difficult to understand or to explain, which for some applications, such as medical decision-making systems, can be a significant disadvantage. Thinking about the explainability aspect, in this work, we present a new technique based on the bag of visual words (BoVW) which, in addition to generating promising explainable methods, has long been the state of the art for generating image representations. The results showed that the presented method BoCS overcomes similar methods and still has the potential to be further explored. (AU)

• Processo FAPESP: 16/17078-0 - Mineração, indexação e visualização de Big Data no contexto de sistemas de apoio à decisão clínica (MIVisBD)
• Beneficiário: Agma Juci Machado Traina
• Modalidade de apoio: Auxílio à Pesquisa - Temático