Content-based image retrieval by combining convolutional neural networks and sparse representation

Sezavar, Amir; Farsi, Hassan; Mohamadzadeh, Sajad

doi:10.1007/s11042-019-7321-1

Cited by 45 publications

(24 citation statements)

References 20 publications

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“…Sezavar et al (Sezavar et al, 2019) proposed a content-based image retrieval approach that uses CNN to extract high-level features. The last layer of Alexnet (Krizhevsky et al, 2017) is used to extract features because the last layer has the smallest feature vector.…”

Section: Deep Learningmentioning

confidence: 99%

“…In this section, six studies that achieved the highest accuracy in global feature extraction will be discussed (Srivastava & Khare, 2017) (Phadikar et al, 2018), local feature extraction (Sarwar et al, 2019) (Yousuf et al, 2018), and machine learning extraction-based approaches (Tzelepi & Tefas, 2018) (Sezavar et al, 2019). All the investigated studies in this section utilize COREL dataset except studies from machines learning approach which utilizes Paris6k and ALOI.…”

Section: Comparison Among the State-of The Art Approachesmentioning

confidence: 99%

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Content-based image retrieval: A review of recent trends

2021

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With the availability of internet technology and the low-cost of digital image sensor, enormous amount of image databases have been created in different kind of applications. These image databases increase the demand to develop efficient image retrieval search methods that meet user requirements. Great attention and efforts have been devoted to improve content-based image retrieval method with a particular focus on reducing the semantic gap between low-level features and human visual perceptions. Due to the increasing research in this field, this paper surveys, analyses and compares the current state-of-the-art methodologies over the last six years in the CBIR field. This paper also provides an overview of CBIR framework, recent low-level feature extraction methods, machine learning algorithms, similarity measures, and a performance evaluation to inspire further research efforts.

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Section: Deep Learningmentioning

confidence: 99%

Section: Comparison Among the State-of The Art Approachesmentioning

confidence: 99%

Content-based image retrieval: A review of recent trends

2021

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“…Many researchers contributed to the image processing task, the semantic gap among the lowest level of image features. In [ 47 ], a robust technique of CNN and sparse representation was proposed. Moreover, a novel technique is presented with an in-depth feature extraction using CNN, and increased image retrieval accuracy and speed using sparse representation.…”

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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“…In [16], the sampling window is used to segment the hand from the image; also, a convolutional neural network is used for classification. These networks are known as one of the most important methods of deep learning [17,18]. Therefore, in this paper, the proposed method is based on convolutional neural networks.…”

Section: Related Workmentioning

confidence: 99%

Estimation of Hand Skeletal Postures by Using Deep Convolutional Neural Networks

Gheitasi

Farsi

Mohamadzadeh

2020

IJE

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Hand posture estimation attracts researchers because of its many applications. Hand posture recognition systems simulate the hand postures by using mathematical algorithms. Convolutional neural networks have provided the best results in the hand posture recognition so far. In this paper, we propose a new method to estimate the hand skeletal posture by using deep convolutional neural networks. To simplify the proposed method and to be more functional, the depth factor is ignored. So only the simple color images of hands are used as inputs of the system. The proposed method is evaluated by using two datasets with high-diversity named Mixamo and RWTH, which include 43,986 and 1160 color images, respectively, where 74% of these images are selected as a training set and, 26% of the rest images are selected as the evaluation set. The experiments show that the proposed method provides better results in both hand posture recognition and detection of sign languages compared to state-of-the-art methods.

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Content-based image retrieval by combining convolutional neural networks and sparse representation

Cited by 45 publications

References 20 publications

Content-based image retrieval: A review of recent trends

Content-based image retrieval: A review of recent trends

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

Estimation of Hand Skeletal Postures by Using Deep Convolutional Neural Networks

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