Fast content-based image retrieval using convolutional neural network and hash function

Varga, Domonkos; Szirányi, Tamás

doi:10.1109/smc.2016.7844637

Cited by 23 publications

(11 citation statements)

References 21 publications

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“…It has been shown that the features extracted using a pretrained CNN are rich and effective for a wide range of computer vision and image processing tasks, such as content-based image retrieval [35], NR image quality assessment [2], and medical image classification [10]. The main contribution and novel aspect of the present study is that we obtain possible solutions for NR-VQA using only the deep features extracted from pretrained CNNs (Inception-V3 [32] and Inception-ResNet-V2 [31]) without depending on manually selected features.…”

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confidence: 99%

No-Reference Video Quality Assessment Based on the Temporal Pooling of Deep Features

Varga

2019

Neural Process Lett

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119

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Video quality assessment (VQA) is an important element of various applications ranging from automatic video streaming to display technology. Furthermore, visual quality measurements require a balanced investigation of visual content and features. Previous studies have shown that the features extracted from a pretrained convolutional neural network are highly effective for a wide range of applications in image processing and computer vision. In this study, we developed a novel architecture for no-reference VQA based on the features obtained from pretrained convolutional neural networks, transfer learning, temporal pooling, and regression. In particular, we obtained solutions by only applying temporally pooled deep features and without using manually derived features. The proposed architecture was trained based on the recently published Konstanz natural video quality database (KoNViD-1k), which contains 1200 video sequences with authentic distortion unlike other publicly available databases. The experimental results obtained based on KoNViD-1k demonstrated that the proposed method performed better than other state-of-the-art algorithms. Furthermore, these results were confirmed by tests using the LIVE VQA database, which contains artificially distorted videos. Keywords No-reference video quality assessment · Convolutional neural network 1 IntroductionMultimedia technology and digital visual signal processing have developed rapidly during recent decades. Digital images and videos are very easy to create, transmit, store, and share. Owing to these developments, the design of reliable video quality assessment (VQA) algorithms has attracted considerable attention. Consequently, VQA has been the focus of many research studies and patents. Furthermore, the vast volume of user-created digital video content has led to the development of numerous VQA applications, which require reliable and effective quality monitoring [39]. B Domonkos Varga

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confidence: 99%

No-Reference Video Quality Assessment Based on the Temporal Pooling of Deep Features

Varga

2019

Neural Process Lett

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119

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“…In addition, dimension reduction can solve the multicollinearity problem by removing redundant features, which is helpful for data visualization. If the dimensions of the data are very high, visualization becomes quite difficult, whereas drawing two-and three-dimensional data is simpler [18]. is paper mainly studies manifold learning-based image dimension reduction algorithm and feature-based image matching algorithm.…”

Section: Contents and Methodsmentioning

confidence: 99%

Research on Multidimensional Image Intelligent Matching Algorithm of Artificial Intelligence Image Recognition Technology

Xiao

Wang²,

Wang

et al. 2021

Mobile Information Systems

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Image matching is a method of matching by analyzing the gray scale and texture information of the reference image and the image to be matched. Firstly, the scale invariant feature transform (SIFT) algorithm has long descriptor time and poor real time, a nonlinear dimension reduction method (LLE) based on local linear embedding is proposed to preserve the nonlinear information in the original data space as much as possible, shorten the running time of the algorithm, and improve the matching accuracy. Second, aiming at the problem that the Euclidean distance takes a large amount of calculation in the matching process, Manhattan distance is proposed to calculate the similarity between the reference image and the image to be matched, so as to further reduce the algorithm time. Through the improved LLE-SIFT algorithm, experimental results show that the algorithm has a high matching rate and improves the matching speed.

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“…The content-based image retrieval (CBIR) is used visual features including image edge, name suitability, color, and texture in input images [ 46 ]. The CNN is applied for image classification to retrieve images using cosine similarity.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Using Isotroping, Laplacing, Eigenvalues Interpolative Binding, and Convolved Determinants with Normed Mapping for Large-Scale Image Retrieval

Kanwal

Ahmad

Khan

et al. 2021

Sensors

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Convolutional neural networks (CNN) are relational with grid-structures and spatial dependencies for two-dimensional images to exploit location adjacencies, color values, and hidden patterns. Convolutional neural networks use sparse connections at high-level sensitivity with layered connection complying indiscriminative disciplines with local spatial mapping footprints. This fact varies with architectural dependencies, insight inputs, number and types of layers and its fusion with derived signatures. This research focuses this gap by incorporating GoogLeNet, VGG-19, and ResNet-50 architectures with maximum response based Eigenvalues textured and convolutional Laplacian scaled object features with mapped colored channels to obtain the highest image retrieval rates over millions of images from versatile semantic groups and benchmarks. Time and computation efficient formulation of the presented model is a step forward in deep learning fusion and smart signature capsulation for innovative descriptor creation. Remarkable results on challenging benchmarks are presented with a thorough contextualization to provide insight CNN effects with anchor bindings. The presented method is tested on well-known datasets including ALOT (250), Corel-1000, Cifar-10, Corel-10000, Cifar-100, Oxford Buildings, FTVL Tropical Fruits, 17-Flowers, Fashion (15), Caltech-256, and reported outstanding performance. The presented work is compared with state-of-the-art methods and experimented over tiny, large, complex, overlay, texture, color, object, shape, mimicked, plain and occupied background, multiple objected foreground images, and marked significant accuracies.

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Fast content-based image retrieval using convolutional neural network and hash function

Cited by 23 publications

References 21 publications

No-Reference Video Quality Assessment Based on the Temporal Pooling of Deep Features

No-Reference Video Quality Assessment Based on the Temporal Pooling of Deep Features

Research on Multidimensional Image Intelligent Matching Algorithm of Artificial Intelligence Image Recognition Technology

Deep Learning Using Isotroping, Laplacing, Eigenvalues Interpolative Binding, and Convolved Determinants with Normed Mapping for Large-Scale Image Retrieval

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