Titolo:  Ten years of relevance score for content based image retrieval
Data di pubblicazione:  2018
Autori:  Putzu, Lorenzo; Piras, Luca; Giacinto, Giorgio
Presenza coautori internazionali:  no
Lingua:  Inglese
Titolo del libro:  Machine Learning and Data Mining in Pattern Recognition
ISBN:  9783319961323
Editore:  Springer Verlag
Volume:  10935
Pagina iniziale:  1
Pagina finale:  15
Numero di pagine:  15
Digital Object Identifier (DOI):  http://dx.doi.org/10.1007/978-3-319-96133-0_9
Codice identificativo Scopus:  2-s2.0-85050488547
URL:  https://www.springer.com/series/558
Revisione (peer review):  Esperti anonimi
Nome del convegno:  14th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2018
Periodo del convegno:  2018
Luogo del convegno:  New York, USA
Abstract:  After more than 20 years of research on Content-Based Image Retrieval (CBIR), the community is still facing many challenges to improve the retrieval results by filling the semantic gap between the user needs and the automatic image description provided by different image representations. Including the human in the loop through Relevance Feedback (RF) mechanisms turned out to help improving the retrieval results in CBIR. In this paper, we claim that Nearest Neighbour approaches still provide an effective method to assign a Relevance Score to images, after the user labels a small set of images as being relevant or not to a given query. Although many other approaches to relevance feedback have been proposed in the past ten years, we show that the Relevance Score, while simple in its implementation, allows attaining superior results with respect to more complex approaches, can be easily adopted with any feature representations. Reported results on different real-world datasets with a large number of classes, characterised by different degrees of semantic and visual intra- e inter-class variability, clearly show the current challenges faced by CBIR system in reaching acceptable retrieval performances, and the effectiveness of Nearest neighbour approaches to exploit Relevance Feedback.
Parole Chiave:  Image description; Image retrieval; Nearest neighbour; Relevance feedback; Theoretical Computer Science; Computer Science (all)
Tipologia: 4.1 Contributo in Atti di convegno

File in questo prodotto:
Non ci sono file associati a questo prodotto.

Questionario e social

Condividi su: