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On the one hand, to improve the performances of CBIRs, that are strictly related to the effectiveness of the descriptors used to represent an image, as they aim at providing the user with images similar to an initial query image. On the other hand, to reduce the semantic gap between the similarity perceived by the user and the similarity computed by the machine, by exploiting an RF mechanism where the user labels the returned images as being relevant or not concerning her interests. Consequently, in this work, we propose a CBIR system based on transfer learning from a CNN trained on a vast image database, thus exploiting the generic image representation that it has already learned. Then, the pre-trained CNN is also fine-tuned exploiting the RF supplied by the user to reduce the semantic gap. In particular, after the user\u2019s feedback, we propose to tune and then re-train the CNN according to the labelled set of relevant and non-relevant images. Then, we suggest different strategies to exploit the updated CNN for returning a novel set of images that are expected to be relevant to the user\u2019s needs. Experimental results on different data sets show the effectiveness of the proposed mechanisms in improving the representation power of the CNN with respect to the user concept of image similarity. Moreover, the pros and cons of the different approaches can be clearly pointed out, thus providing clear guidelines for the implementation in production environments.<\/jats:p>","DOI":"10.1007\/s11042-020-09292-9","type":"journal-article","created":{"date-parts":[[2020,7,20]],"date-time":"2020-07-20T23:12:11Z","timestamp":1595286731000},"page":"26995-27021","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["Convolutional neural networks for relevance feedback in content based image retrieval"],"prefix":"10.1007","volume":"79","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5361-8793","authenticated-orcid":false,"given":"Lorenzo","family":"Putzu","sequence":"first","affiliation":[]},{"given":"Luca","family":"Piras","sequence":"additional","affiliation":[]},{"given":"Giorgio","family":"Giacinto","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,7,21]]},"reference":[{"key":"9292_CR1","unstructured":"Ashraf R, Ahmed M, Ahmad U, Habib M, Jabbar S, Naseer K (2020) Mdcbir-mf: multimedia data for content-based image retrieval by using multiple features. 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