Biogeography particle swarm optimization based counter propagation network for sketch based face recognition

Agrawal, Suchitra; Singh, Rajeev Kumar; Singh, Uday Pratap; Jain, Sanjeev

doi:10.1007/s11042-018-6542-z

Cited by 8 publications

(5 citation statements)

References 48 publications

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“…In [9], a feature fusion-based sketch recognition method is proposed based on convolutional neural networks. In [10], a BPSO-based backpropagation network is used to process sketches for feature recognition. In [11], the order of sketch strokes is considered and the stroke-level annotation can be solved for multiple sketch categories.…”

Section: Related Researchmentioning

confidence: 99%

Research on hand sketch recognition method based on Sketch-AlexNet

Wang,

Zhang,

Zhao

2023

Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023)

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With the advancement of artificial intelligence and image processing technology, hand-drawn sketches can be utilized in diverse applications, such as digital creativity, design, and education, among others, highlighting their vast potential in various fields. However, existing challenges in hand-sketch recognition such as low accuracy and efficiency necessitate the development of an improved recognition method. To address these challenges, a sketch recognition method based on Sketch-AlexNet is proposed. Specifically, a larger first layer convolutional kernel is selected to enhance feature extraction ability, while a smaller step size is used to minimize information loss. Moreover, a set of multiplicative convolutional kernels is introduced to replace the original convolutional structure, enabling the network to extract features across shallow to deep hierarchies. The Sketch-AlexNet model is trained on a self-built hand-drawn sketch dataset, yielding a 9.8% accuracy improvement, better recognition results, and corresponding advancements in recognition speed and stability.

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Section: Related Researchmentioning

confidence: 99%

Research on hand sketch recognition method based on Sketch-AlexNet

Wang,

Zhang,

Zhao

2023

Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023)

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“…Images have always been an important medium for people to convey information. Compared with other human organs, the image received by the normal human eye daily has more information, and the processing of images and other visual information accounts for about 70% of people's daily cerebral cortical activities [1,2]. With the emergence of digital cameras and the gradual popularization of the Internet, massive amounts of image and video data are uploaded to the network every day and spread quickly on the network [3].…”

Section: Introductionmentioning

confidence: 99%

Virtual Reality Image Technology Assists Training Optimization of Motion Micro-Time Scale

Cao

Shang

2020

IEEE Access

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Virtual reality image local features are highly repetitive and are not easily affected by target occlusion, so research on virtual reality image local feature extraction techniques has received increasing attention. For the purpose of global scale selection, this article considers the virtual reality image in the global scope from the theoretical perspectives of the continuity of virtual reality images, the repeatability of virtual reality image features, the scale of scene virtual reality images in the global scope, and the visual saliency. This paper gives a general framework of multi-scale geometric analysis and particle swarm optimization virtual reality image matching algorithm. For the problem of virtual reality image matching, the original particle swarm optimization algorithm is improved. A more reasonable inertia weight change formula, a method for selecting the optimal solution when two objectives are optimized at the same time, and a method for selecting a reasonable input are designed. In this paper, performance evaluation experiments are performed on several feature description algorithms. The experimental results show that the descriptors constructed by these feature description algorithms are significantly more robust than SIFT (Scale Invariant Feature Transform) descriptors. The improved multi-scale geometric analysis and particle swarm optimization are robust due to the introduction of multiple support domains. It has the best discriminative ability, but at the same time, the introduction of multiple support domains leads to the worst real-time algorithm; DGOH (Dual Gradient Orientation Histogram) algorithm is more robust than FRDOH (Fast Representation Based on a Double Orientation Histogram) and SIFT algorithm, and DGOH algorithm has the best real-time performance. INDEX TERMS virtual reality images; particle swarm optimization; multi-scale geometric analysis; inertial weights; feature description.

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“…As such this work belong to component-based methods. Several component-based face sketch recognition methods were discussed in the literature [10][11][12][13][14][15]. For instances, Hu et al [11] adopt Active Shape Model (ASM) to locate face sketch facial components.…”

Section: Introductionmentioning

confidence: 99%

“…In spite of good results that were achieved, automatic approach without user feedback is more challenge. A recent studies were discussed by Liu et al [10], Kute et al [13], and [14]. The fusion of SIFT features with histogram of oriented gradient (HOG) was discussed in [10].…”

Section: Introductionmentioning

confidence: 99%

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Component-based face sketch recognition using an enhanced evolutionary optimizer

2019

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The main aim of this work is to develop a component-based face sketch recognition model. The proposed model adopts an enhanced evolutionary optimizer (EEO) to perform the task of face sketched components localization. EEO is applied to an unknown input sketch to make an automatic localization for its components i.e. eyes, nose, and mouth. After that, HOG features are extracted, and cosine similarity measure is computed to find the best components location. EEO integrates Q-learning algorithm with the simulated annealing (SA) algorithm as a single mode. The Q-learning algorithm is used to control the execution of SA parameters i.e. temperature and the mutation rate at run time. The proposed approach was evaluated on three face sketch recognition benchmark problem which are LFW, AR, and CUHK. The experimental results show that EEO significantly outperform SA as well as other well-known meta-heuristic optimization algorithms such as PSO, Harmony, and MVO.

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Biogeography particle swarm optimization based counter propagation network for sketch based face recognition

Cited by 8 publications

References 48 publications

Research on hand sketch recognition method based on Sketch-AlexNet

Research on hand sketch recognition method based on Sketch-AlexNet

Virtual Reality Image Technology Assists Training Optimization of Motion Micro-Time Scale

Component-based face sketch recognition using an enhanced evolutionary optimizer

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