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Main Title: Retrieving Images with Generated Textual Descriptions
Author(s): Hoxha, Genc
Melgani, Farid
Demir, Begüm
Type: Conference Object
Language Code: en
Abstract: This paper presents a novel remote sensing (RS) image retrieval system that is defined based on generation and exploitation of textual descriptions that model the content of RS images. The proposed RS image retrieval system is composed of three main steps. The first one generates textual descriptions of the content of the RS images combining a convolutional neural network (CNN) and a recurrent neural network (RNN) to extract the features of the images and to generate the descriptions of their content, respectively. The second step encodes the semantic content of the generated descriptions using word embedding techniques able to produce semantically rich word vectors. The third step retrieves the most similar images with respect to the query image by measuring the similarity between the encoded generated textual descriptions of the query image and those of the archive. Experimental results on RS image archive composed of RS images acquired by unmanned aerial vehicles (UAVs) are reported and discussed.
Issue Date: 14-Nov-2019
Date Available: 25-Nov-2019
DDC Class: 006 Spezielle Computerverfahren
Subject(s): image retrieval
image textual description generation
semantic gap
unmanned aerial vehicles
Proceedings Title: IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Publisher Place: New York, NY
Publisher DOI: 10.1109/IGARSS.2019.8899321
Page Start: 5812
Page End: 5815
EISSN: 2153-7003
ISBN: 978-1-5386-9154-0
ISSN: 2153-6996
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Appears in Collections:FG Remote Sensing Image Analysis Group » Publications

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