Istituto di Scienza e Tecnologie dell'Informazione     
Amato G., Falchi F., Gennaro C., Vadicamo L. Deep permutations: deep convolutional neural networks and permutation-based indexing. In: SISAP 2016 - Similarity Search and Applications. 9th International Conference (Tokyo, Japan, 24-26 October 2016). Proceedings, pp. 93 - 106. Amsaleg L., Houle M., Schubert E (eds.). (Lecture Notes in Computer Science, vol. 9939). Springer, 2016.
The activation of the Deep Convolutional Neural Networks hidden layers can be successfully used as features, often referred as Deep Features, in generic visual similarity search tasks. Recently scientists have shown that permutation-based methods offer very good performance in indexing and supporting approximate similarity search on large database of objects. Permutation-based approaches represent metric objects as sequences (permutations) of reference objects, chosen from a predefined set of data. However, associating objects with permutations might have a high cost due to the distance calculation between the data objects and the reference objects. In this work, we propose a new approach to generate permutations at a very low computational cost, when objects to be indexed are Deep Features. We show that the permutations generated using the proposed method are more effective than those obtained using pivot selection criteria specifically developed for permutation-based methods.
URL: http://dx.doi.org/10.1007/978-3-319-46759-7_7
DOI: 10.1007/978-3-319-46759-7_7
Subject Similarity search
Permutation-based indexing
Deep convolutional neural network
H.3.3 INFORMATION STORAGE AND RETRIEVAL. Information Search and Retrieval

Icona documento 1) Download Document PDF

Icona documento Open access Icona documento Restricted Icona documento Private


Per ulteriori informazioni, contattare: Librarian http://puma.isti.cnr.it

Valid HTML 4.0 Transitional