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Neural Processing Letters
Erschienen in: Neural Processing Letters 3/2023

13.01.2023

Efficient Deep Feature Based Semantic Image Retrieval

verfasst von: Suneel Kumar, Manoj Kumar Singh, Manoj Mishra

Erschienen in: Neural Processing Letters | Ausgabe 3/2023

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Abstract

Explosive growth of multimedia content leads to massive amount of images which are uploaded every day in the cyber world, medical imaging repository, and other areas. Retrieval of image of interest from internet or huge repository of image data set is still challenging and an open problem. Thus, content based image retrieval (CBIR) systems are developed. Success of CBIR system mainly depends on the image features which used for indexing and similarity measurement. CBIR system developed in deep learning framework has recently demonstrated promising results. In this paper, we present a modified-VGG16 (M-VGG16), deep convolution neural network (DCNN), for image feature extraction. These features are used for image indexing and retrieval in the CBIR system. In M-VGG16, we added \(1\times 1\) and \(3\times 3\) convolution kernels into input layer, followed by depth concatenation of the output of both convolution kernels. We apply principal component analysis (PCA) on the features obtained from M-VGG16 for getting robust features with reduced dimension, this leads to the compact image indexing and better retrieval performance. The performance M-VGG16 and M-VGG16 with PCA (M-VGG16 + PCA) is evaluated on benchmark datasets Corel-1k, Corel-10k, Coil-10k, and KADID-10k. Our proposed M-VGG16 + PCA model shows better results in terms of performance metric: precision, recall, and F1-score, when compared to state-of-art model AlexNet , VGG16, and other model proposed using DCNN and hand-crafted feature together. Moreover, in M-VGG16 + PCA the dimension of feature vector is 88% lesser than the M-VGG16 model.
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Metadaten
Titel
Efficient Deep Feature Based Semantic Image Retrieval
verfasst von
Suneel Kumar
Manoj Kumar Singh
Manoj Mishra
Publikationsdatum
13.01.2023
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 3/2023
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-022-11079-y

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