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CBIR Systems: Techniques and Challenges

CBIR Systems: Techniques and Challenges

引用

International conference on communications and Signal processing

作者： Niveditha Arunkumar A Ranjith Ram Department of Electronics and Communication Government College of Engineering Kannur Kerala India faculty of Department of Electronics and Communication Government College of Engineering Kannur Kerala India

ISBN: (数字)9781728149882

ISBN: (纸本)9781728149899

The vigorous growth and continual intelligibility of visual information on the cyberspace have led to the richness of research projects in image search and retrieval. With the lack of concern on the visual contents of an image, the text based search techniques for image retrieval often suffers variability between the text words and visual content. Hence Content Based image Retrieval (CBIR) has achieved a great appeal as it models the perceptual content of an image to identify similar relevant images. This paper aims at discussing the evolution of retrieval techniques, paying particular attention on development, merits, demerits and challenges of the image retrieval. It focuses on reviewing the already existing techniques and its related issues. The enormous development of image data necessitates the need for the expansion of research and helps in the progress of efficient and accurate image retrieval. Also, CBIR maintains a sustained growth in the research field.

关键词： Feature extraction image color analysis image segmentation image retrieval visualization Semantics

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Extreme image coding via multiscale autoencoders with generative adversarial optimization 34

Extreme image coding via multiscale autoencoders with genera...

引用

34th IEEE International conference on visual communications and image processing, VCIP 2019

作者： Huang, Chao Liu, Haojie Chen, Tong Shen, Qiu Ma, Zhan Nanjing University Vision Lab China

ISBN: (纸本)9781728137230

We propose a MultiScale AutoEncoder (MSAE) based extreme image coding/compression framework to offer visually pleasing reconstruction at a very low bitrate. Our method leverages the "priors" at different resolution scale to improve the compression efficiency, and also employs the generative adversarial network (GAN) with multiscale discriminators to perform the end-To-end trainable rate-distortion optimization. We compare the perceptual quality of our reconstructions with traditional compression algorithms using High-Efficiency Video Coding (HEVC) based Intra Profile and JPEG2000 on the public Cityscapes, ADE20K and Kodak datasets, demonstrating the significant subjective quality improvement. However, objective measurements, such as PSNR, SSIM, etc, are often deteriorated by applying the generative adversarial optimization. © 2019 IEEE.

关键词： image coding

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image-Based End-To-End Neural Network for Dense Disparity Estimation 34

Image-Based End-To-End Neural Network for Dense Disparity Es...

引用

34th IEEE International conference on visual communications and image processing, VCIP 2019

作者： Sun, Shuqiao Liu, Rongke Du, Qiuchen Sun, Shantong Kang, Shaoli Beihang University School of Electronic and Information Beijing China

ISBN: (纸本)9781728137230

Stereo matching is a challenging yet important task to various computer vision applications, e.g. 3D reconstruction, augmented reality, and autonomous vehicles. In this paper, we present a novel image-based convolutional neural network (CNN) for dense disparity estimation using stereo image pairs. In order to achieve precise and robust stereo matching, we introduce a feature extraction module that learns both local and global information. These features are then passed through an hour-glass structure to generate disparity maps from lower resolution to full resolution. We test the proposed method in several datasets including indoor scenes and synthetic scenes. Experimental results demonstrate that the proposed method outperforms the state-of-The-Art methods in several datasets. © 2019 IEEE.

关键词： Feature extraction

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No-reference stereoscopic image quality assessment based on dilation convolution 34

No-reference stereoscopic image quality assessment based on ...

引用

34th IEEE International conference on visual communications and image processing, VCIP 2019

作者： Zhao, Ping Li, Sumei Chang, Yongli Tianjin University School of Electrical and Information Engineering Tianjin China

ISBN: (纸本)9781728137230

Over the years, with the popularization of 3D technology, the demands of accurate and efficient 3D image quality evaluation (SIQA) methods are increasing constantly. Due to the wide application of CNN, CNN-based SIQA methods emerge one after another. However, current methods only consider a single scale or resolution, and some CNN-based methods directly take left and right views as an input of the network ignoring the visual fusion mechanism. In this work, a multi-scale no-reference SIQA method is proposed based on dilation convolution neural network (DCNN). Different from other CNN-based SIQA methods, the proposed one uses dilation convolution to imitate different scale of information processing fields in the human brain. Instead of left or right image, the cyclopean image generated by a new method is used as the input of the network. Moreover, the proposed multi-scale unit significantly can reduce computational parameters and computational complexity. Experimental results on two public databases show that the proposed model is superior to the state-of-The-Art no-reference SIQA methods. © 2019 IEEE.

关键词： Convolution

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Fpga based video transcoding system with 2k-4k super-resolution conversion 34

Fpga based video transcoding system with 2k-4k super-resolut...

引用

34th IEEE International conference on visual communications and image processing, VCIP 2019

作者： Wei, Yuzhuo Chen, Li Xie, Rong Song, Li Zhang, Xiaoyun Gao, Zhiyong Shanghai Jiao Tong University Institute of Image Communication and Network Engineering Shanghai China

ISBN: (纸本)9781728137230

We present a FPGA-based system supporting video stream transcoding with 2k full high-definition (FHD) video to 4k ultra high-definition (UHD) video super-resolution(SR) conversion. Our system focuses on building a functional pipeline with convolutional neural network (CNN) accelerator and real-Time video codec unit for converting H.264 video stream to H.265/HEVC video stream. The overall video processing system can be used as an important plug-in module in the video streaming network to improve the video stream service quality. © 2019 IEEE.

关键词： Video streaming

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Unpaired image-To-Sketch Translation Network for Sketch Synthesis 34

Unpaired Image-To-Sketch Translation Network for Sketch Synt...

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34th IEEE International conference on visual communications and image processing, VCIP 2019

作者： Zhang, Yue Su, Guoyao Qi, Yonggang Yang, Jie Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781728137230

image-To-sketch translation is to learn the mapping between an image and a corresponding human drawn sketch. Machine can be trained to mimic the human drawing process using a training set of aligned image-sketch pairs. However, to collect such paired data is quite expensive or even unavailable for many cases since sketches exhibit various level of abstractness and drawing preferences. Hence we present an approach for learning an image-To-sketch translation network via unpaired examples. A translation network, which can translate the representation in image latent space to sketch domain, is trained in unsupervised setting. To prevent the problem of representation shifting in cross-domain translation, a novel cycle+ consistency loss is explored. Experimental results on sketch recognition and sketch-based image retrieval demonstrate the effectiveness of our approach. © 2019 IEEE.

关键词： Drawing (graphics)

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Medical images Segmentation Using a Novel Level Set Model with Laplace Kernel Function 8th

Medical Images Segmentation Using a Novel Level Set Model wi...

引用

8th International conference on communications, Signal processing, and Systems, CSPS 2019

作者： Song, Jianhua Zhang, Zhe Zhen, Jiaqi College of Electronic Engineering Heilongjiang University Harbin China College of Physics and Information Engineering Minnan Normal University Fujian China

ISBN: (纸本)9789811394089

Medical image segmentation is a complex study due to its disadvantages such as noise, low-contrast, intensity inhomogeneity, and so on. A novel level set model was proposed in this study to segment medical images accurately. The kernel function used to determine the size of neighborhood of central pixel was modified by Laplace kernel function, which is insensitive to the choice of parameters and is more suitable for segmenting medical images. Compared with several state-of-the-art models, both visual and objective experiments can demonstrate the performance and superiority of the novel level set model. © 2020, Springer Nature Singapore Pte Ltd.

关键词： image segmentation

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Improving Compression Artifact Reduction via End-to-End Learning of Side Information

Improving Compression Artifact Reduction via End-to-End Lear...

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IEEE visual communications and image processing (VCIP)

作者： Haichuan Ma Dong Liu Feng Wu CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei China

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

We propose to improve neural network-based compression artifact reduction by transmitting side information for the neural network. The side information consists of artifact descriptors that are obtained by analyzing the original and compressed images in the encoder. In the decoder, the received descriptors are used as additional input to a well-designed conditional post-processing neural network. To reduce the transmission overhead, the entire model is optimized under the rate-distortion constraint via end-to-end learning. Experimental results show that introducing the side information greatly improves the ability of the post-processing neural network, and improves the rate-distortion performance.

关键词： image coding Neural networks Decoding Training Feature extraction Computational modeling Transform coding

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From Low to Super Resolution and Beyond

From Low to Super Resolution and Beyond

引用

IEEE visual communications and image processing (VCIP)

作者： Chi-Wah Kok Wing-Shan Tam CVX Semiconductor Pty Ltd Adelaide South Australia Australia

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

The tutorial starts with an introduction of digital image interpolation, and single image super-resolution. It continues with the definition of various image interpolation performance measurement indices, including both objective and subjective indices. The core of this tutorial is the application of covariance based interpolation to achieve high visual quality image interpolation and single image super-resolution results. Layer on layer, the covariance based edge-directed image interpolation techniques that makes use of stochastic image model without explicit edge map, to iterative covariance correction based image interpolation. The edge based interpolation incorporated human visual system to achieve visually pleasant high resolution interpolation results. On each layer, the pros and cons of each image model and interpolation technique, solutions to alleviate the interpolation visual artifacts of each techniques, and innovative modification to overcome limitations of traditional edge-directed image interpolation techniques are presented in this tutorial, which includes: spatial adaptive pixel intensity estimation, pixel intensity correction, error propagation mitigation, covariance windows adaptation, and iterative covariance correction. The tutorial will extend from theoretical and analytical discussions to detail implementation using MATLAB. The audience shall be able to bring home with implementation details, as well as the performance and complexity of the interpolation algorithms discussed in this tutorial.

关键词： Interpolation Tutorials Matlab Digital images Australia image edge detection Consumer electronics

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NIR image colorization with graph-convolutional neural networks

NIR image colorization with graph-convolutional neural netwo...

引用

IEEE visual communications and image processing (VCIP)

作者： Diego Valsesia Giulia Fracastoro Enrico Magli Politecnico di Torino Italy

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

Colorization of near-infrared (NIR) images is a challenging problem due to the different material properties at the infared wavelenghts, thus reducing the correlation with visible images. In this paper, we study how graph-convolutional neural networks allow exploiting a more powerful inductive bias than standard CNNs, in the form of non-local self-similiarity. Its impact is evaluated by showing how training with mean squared error only as loss leads to poor results with a standard CNN, while the graph-convolutional network produces significantly sharper and more realistic colorizations.

关键词： Convolution conferences Standards Pattern recognition Computer vision Computer architecture Task analysis

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