Deep learning feature map for content based image retrieval system for remote sensing application. (2016)

• Record Type: Journal Article
• Authors: Jain, Swati; Zaveri, Tanish; Prajapati, Kinjal; Patel, Shailee
• Is Part Of: International journal of image mining. Volume 2:Number 1(2016)
• Page Start: 1
• Page End: 11
• Publication Date: 2016
• Subjects: CBIR -- convolutional neural networks -- CNNs -- deep learning -- feature maps -- content based image retrieval -- remote sensing -- entropy -- contrast -- similarity measures; Image processing -- Periodicals; Data mining -- Periodicals; 006.42
• Languages: English
• ISSNs: 2055-6039

Abstract

This paper proposes a model for content based image retrieval system (CBIR), in which handcrafted feature set is replaced with feature set learnt from deep learning, convolutional neural network (CNN) for image retrieval. Feature map obtained from CNN is of high dimension, which makes the matching process expensive in terms of time and computation. Hence to recapitulate information in smaller dimension statistical values of the feature maps are calculated. Statistical values like entropy and contrast are taken as characteristic value of each feature map, and these values are used as features for similarity measure in CBIR system. The retrieval performance are compared, when feature map from deep neural net is considered and when statistical values of the feature map are used. The performance parameter considered are normalised rank and number of relevant images retrieved. The proposed approach is experimented with UC Merced Landuse Landcover Dataset and the results obtained establishes that statistical features give better results.