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image annotation algorithm based on semantic similarity and multi-features 1

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3rd International conference on Smart Computing and communications, SmartCom 2018

作者： Ni, Jingxiu Wang, Dongxing Zhang, Guoying Sun, Yanchao Xu, Xinkai Beijing Union University Beijing100101 China School of Mechanical Electronic and Information Engineering China University of Mining and Technology Beijing100083 China

ISBN: (数字)9783030057558

ISBN: (纸本)9783030057541

The paper proposed an image annotation algorithm based on semantic similarity and multi-feature fusion. The annotation algorithm draws lessons from the method of semantic extraction in natural language processing, and establishes the corresponding semantic trees for some common scenes. The scene semantic tree is constructed based on the visual features of the specific scene in the image set. Firstly, the visual features of scene images are extracted, and then the visual features are clustered by fuzzy clustering. According to the clustering results, the images are grouped, clustered at different nodes according to visual features, and the images are further grouped. After the scene semantic tree is constructed, the algorithm will extract the visual features of the image to be annotated. Furthermore, the image moves from the item node to a leaf node in the scene semantic tree according to its visual features, and the semantic keywords which appear in the route constitute the tags of the image. © 2018, Springer Nature Switzerland AG.

关键词： image annotation

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LSB-Based visual image Encryption Scheme in Cloud Environment

LSB-Based Visual Image Encryption Scheme in Cloud Environmen...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Sifei Zheng Xiaolong Liu Riqing Chen Shyan-Ming Yuan Chia-Chen Lin College of Computer and Information Sciences Digital Fujian Institute of Big Data for Agriculture and Forestry Fujian Agriculture and Forestry University Fuzhou China Department of Computer Science National Chiao Tung University Hsinchu Taiwan Department of Computer Science and Information Management Providence University Taichung Taiwan

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Preventing multimedia data in cloud environment from leakage is a significant and imperative issue in the modern information-oriented society. Many encryption schemes have been proposed to convert original images into noise-like images. However, the noise-like images are conspicuous visual signs to attract the attention of attackers, and thus lead to large quantities of attacks. In order to solve the problem, this paper propose a visual encryption scheme to convert an original image into another visually meaningful image to elude the attacker's attention and ensure the security of the image. image data security is implemented by pre-encryption procedure and the meaningful appearance of encrypted image is based on Least significant bit (LSB) substitution, respectively. The experimental results demonstrate the superiority of the proposed scheme in terms of excellent encryption performance. The proposed scheme can protect original images that require a higher level of security compared with existing encryption schemes.

关键词： Encryption Cloud computing visualization Transforms Agriculture

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Artificial Neural Networks and Fuzzy Logic for Specifying the Color of an image Using Munsell Soil-Color Charts 17th

Artificial Neural Networks and Fuzzy Logic for Specifying th...

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17th International conference on Information processing and Management of Uncertainty in Knowledge-Based Systems (IPMU)

作者： Carmen Pegalajar, Maria Sanchez-Maranon, Manuel Baca Ruiz, Luis G. Mansilla, Luis Delgado, Miguel Univ Granada Dept Comp Sci & Artificial Intelligence C Periodista Daniel Saucedo Aranda S-N E-18071 Granada Spain Univ Granada Dept Soil Sci & Agr Chem Campus Fuentenueva S-N E-18071 Granada Spain

ISBN: (纸本)9783319914732;9783319914725

The Munsell soil-color charts contain 238 standard color chips arranged in seven charts with Munsell notation. They are widely used to determine soil color by visual comparison, seeking the closest match between a soil sample and one of the chips. The Munsell designation of this chip (hue, value, and chroma) is assigned to the soil under study. However, the available chips represent only a subset of all possible soil colors, in which the visual appearance for an observer is usually intermediate between several chips. Our study proposes an intelligent system which combines two Soft Computing Techniques (Artificial Neural Networks and Fuzzy Logic Systems) aimed at finding a set of chips as similar as possible to a given soil sample. This is under the precondition that the soil sample is an image taken by a digital camera or mobile phone. The system receives an image as input and returns a set of color-chip designations as output.

关键词： Munsell soil-color charts Artificial Neural Networks Fuzzy logic

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Loop Closure Detection Based on Improved Hybrid Deep Learning Architecture

Loop Closure Detection Based on Improved Hybrid Deep Learnin...

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IEEE International Symposium on Parallel and Distributed processing with Applications and IEEE International conference on Ubiquitous Computing and communications (ISPA/IUCC)

作者： Yuan Liu Rong Xiang Qieshi Zhang Ziliang Ren Jun Cheng Guilin University of Electronic Technology Guilin China Key Laboratory of Human-Machine Intelligence-Synergy Shenzhen China

ISBN: (纸本)9781728152103

Loop closure detection plays a vital role in the visual Simultaneous Localization and Mapping (SLAM) system. Traditional Bag-of-visual-Word (BoVW) methods for loop closure detection has been successfully applied to various SLAM systems. The Convolutional Neural Networks (ConvNets) based methods can automatically learn feature representation from original image, and it is more robust to illumination changes. However, these two methods all have some limitations. In order to overcome the shortcomings of the traditional methods extract insufficient features, and the ConvNets based methods unable to adapt complex environment changes, in this paper, we propose an improved hybrid deep learning architecture (HDLA) to generate high-level semantic image features for loop closure detection specifically. This network is based on hybrid ConvNet modified by us to especially cope with robust and real-time feature extraction. We present extensive experiments on three real world datasets to evaluate each of the specific challenges in loop closure detection. The results demonstrate that the proposed method achieves superior performances and provides a feasible solution for loop closure detection.

关键词： convolutional neural nets feature extraction image classification image representation learning (artificial intelligence) object detection robot vision SLAM (robots) robot vision Feature extraction image representation Object detection image classification surgical closure Loops Contraceptive Coil Feasible Solution original image simultaneous localization and mapping

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Efficient Weighted Kernel Sharing Convolutional Neural Networks

Efficient Weighted Kernel Sharing Convolutional Neural Netwo...

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33rd IEEE International conference on visual communications and image processing (IEEE VCIP)

作者： Zhou, Helong Chen, Yie-Tarng Zhang, Jie Fang, Wen-Hsien Natl Taiwan Univ Sci & Technol Dept Elect & Comp Engn Taipei Taiwan Chinese Acad Sci Inst Comp Technol Beijing Peoples R China Seetatech Techonol Co Ltd Beijing Peoples R China

ISBN: (纸本)9781538644584

To lessen the redundancy of convolutional kernels, this paper proposes a new convolutional structure, i.e., weighted kernel sharing convolution (WKSC), which gathers the inputs with the same kernel, so the inputs in each group can share the same convolutional kernel. Also, an extra weighting is imposed for each input channel before the sharing process to manifest its diversity. As a consequence, the number of kernels can be greatly reduced, leading to a reduction of model parameters and the speedup of inference. Moreover, WKSC can be combined with other existing compression models such as depthwise separable convolutions, resulting in a more compressed architecture. Extensive experiments on CIFAR-100 and imageNet classification demonstrate the effectiveness of the new approach in both computation cost and the parameters required compared with the state-of-the-art works.

关键词： Compact convolution filters model compression and acceleration image classification deep learning

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Underwater image Enhancement with a Total Generalized Variation Illumination Prior

Underwater Image Enhancement with a Total Generalized Variat...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Zhengjie Zhao Yuxiang Dai Peixian Zhuang School of Electronic and Information Engineering Nanjing University of Information Science and Technology Nanjing China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

In order to promote the visual quality of degraded underwater images, we develop an innovative variational model based on Retinex with a total generalized variation (TGV) prior on the illumination. The TGV prior is adopted to approximate piecewise smoothness and piecewise linear smoothness of the illumination, which combines the first-order and second-order total variation (TV) to model the variation of illumination. When adopting this illumination prior in the Retinex-based algorithm, the illumination and reflection are well separated, and underwater enhanced results appear more natural and their details and edges are better preserved. Then an efficient iterative optimization method is derived to settle the proposed model via alternately calculating the illumination and the reflection simultaneously. Numerous experiments on both visual results and objective metrics demonstrate the superiority of our method compared with several underwater enhancement methods. In addition, the proposed method can be extended for dehazing, sandstorm removal and low illumination image enhancement, which can illustrate better capacity of our model.

关键词： Lighting image color analysis Mathematical model Optimization image enhancement visualization Adaptation models

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Medical image Denoising Using Synergistic Fibroblast Optimization Based Weighted Median Filter 2

Medical Image Denoising Using Synergistic Fibroblast Optimiz...

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2nd International conference on Advances in Electronics, Computers and communications (ICAECC)

作者： Dhivyaprabha, T. T. Jayashree, G. Subashini, P. Avinashilingam Inst Home Sci & Higher Educ Women Dept Comp Sci Coimbatore Tamil Nadu India Kumaraguru Coll Technol Dept Informat Technol Coimbatore Tamil Nadu India

ISBN: (纸本)9781538637852

image analysis involves processing and extraction of knowledge from images which is significantly useful in the large scale of medical and engineering applications. image denoising is primarily applied in image analysis for degradation of noise, and thus improves the visual quality of images for information retrieval process. In the recent scenarios, due to the increase of complexity and diversity of digital images, removal of noise present in complicated images using classical filters becomes a quite challenge. The objective of this paper is to investigate the performance efficiency of a newly developed Synergistic Fibroblast Optimization based Weighted Median Filter (SFO-WMF) for medical image analysis. Experiments are carried out with benchmark images, real time Magnetic Resonance Imaging (MRI) images, ultrasound breast cancer images and compared with conventional filters, namely, mean filter, median filter, wiener filter, Gaussian filter and weighted median filter. The performances of filters are validated using standard performance metrics and computational results demonstrated that the novel filter produces promising results and it outperforms than conventional filters in both qualitative and quantitative perspectives.

关键词： denoising filtering technique medical image analysis MRI dataset Synergistic Fibroblast Optimization (SFO) ultrasound dataset

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Meaning and Uncertainty Inherent in Understanding images, Spatial-Taxon Hierarchy, Word Annotation and Relevant Context 17th

Meaning and Uncertainty Inherent in Understanding Images, Sp...

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17th International conference on Information processing and Management of Uncertainty in Knowledge-Based Systems (IPMU)

作者： Barghout, Lauren Univ Calif Berkeley Elect Engn & Comp Sci BISC Berkeley CA 94720 USA

ISBN: (纸本)9783319914794;9783319914787

This paper explores the meaning and uncertainty inherent in (a) understanding image hierarchies;(b) describing them with words;and (c) navigating the abstraction context of the viewer. A spatial-taxon hierarchy, a standardized scene architecture, partitions an image into a foreground, subject and salient objects and/or sub-objects. The introduction starts with a thought experiment (Thought experiments, borrowed from the model-theoretic isomorphism standard of structure-mapping theory, enable readers to compare two systems thought to be similar. It's a form of inductive reasoning that expands knowledge in the face of uncertainty (Holland et al. 1986 [13]) by providing an explicit representation of how two systems are similar. Though the conclusion that the two systems do share an isomorphic structure can only be supported via various degrees of truth (fuzzy membership), it establishes its plausibility. Analogical reasoning is natural to human thought and communication making it useful for scientific papers.) based on a poem & an image landscape. The thought experiment is intended to provide analogical inference as scaffolding for the rest of the paper. The results of experimental data of human annotated spatial-taxon and corresponding word descriptions of two images are presented. The experimental results are analyzed in terms of spatial-taxon designation and the meaning & uncertainty presented by the human annotations. The results support the fuzzy spatial-taxon hierarchy of human scene perception described by other works, show that word descriptions depend on spatial-taxon designation and that long tail word distributions require unbounded possibility with semantic uncertainty (type 2 fuzzy sets) for the word counts in the probability distribution. Deep learning image recognition, Zadeh information restriction principal, Shannon's distinction between information content and semantics, customized image descriptions and fuzzy inference techniques are explored.

关键词： Segmentation Recognition Classification image labeling Semantic Spatial-taxon Fuzzy inference Possibility theory Deep learning Information theory visual-taxometrics Machine vision Human vision Attention visual & image cognition

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image Analysis in Light Sheet Fluorescence Microscopy images of Transgenic Zebrafish Vascular Development 22nd

Image Analysis in Light Sheet Fluorescence Microscopy Images...

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22nd conference on Medical image Understanding and Analysis (MIUA)

作者： Kugler, Elisabeth Chico, Timothy Armitage, Paul Univ Sheffield Dept Infect Immun & Cardiovasc Dis Fac Med Sheffield S10 2TN S Yorkshire England Univ Sheffield Bateson Ctr Sheffield S10 2TN S Yorkshire England

ISBN: (纸本)9783319959214;9783319959207

The zebrafish has become an established model to study vascular development and disease in vivo. However, despite it now being possible to acquire high-resolution data with state-of-the-art fluorescence microscopy, such as lightsheet microscopy, most data interpretation in pre-clinical neurovascular research relies on visual subjective judgement, rather than objective quantification. Therefore, we describe the development of an image analysis workflow towards the quantification and description of zebrafish neurovascular development. In this paper we focus on data acquisition by lightsheet fluorescence microscopy, data properties, image pre-processing, and vasculature segmentation, and propose future work to derive quantifications of zebrafish neurovasculature development.

关键词： 3D Analysis Development In vivo Light sheet fluorescence microscopy (LSFM) Segmentation Vasculature Zebrafish

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visual Object Networks: image Generation with Disentangled 3D Representation 32

Visual Object Networks: Image Generation with Disentangled 3...

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32nd conference on Neural Information processing Systems (NIPS)

作者： Zhu, Jun-Yan Zhang, Zhoutong Zhang, Chengkai Wu, Jiajun Torralba, Antonio Tenenbaum, Joshua B. Freeman, William T. MIT CSAIL Cambridge MA 02139 USA MIT CSAIL Google Cambridge MA 02139 USA

Recent progress in deep generative models has led to tremendous breakthroughs in image generation. However, while existing models can synthesize photorealistic images, they lack an understanding of our underlying 3D world. We present a new generative model, visual Object Networks (VON), synthesizing natural images of objects with a disentangled 3D representation. Inspired by classic graphics rendering pipelines, we unravel our image formation process into three conditionally independent factors-shape, viewpoint, and texture-and present an end-to-end adversarial learning framework that jointly models 3D shapes and 2D images. Our model first learns to synthesize 3D shapes that are indistinguishable from real shapes. It then renders the object's 2.5D sketches (i.e., silhouette and depth map) from its shape under a sampled viewpoint. Finally, it learns to add realistic texture to these 2.5D sketches to generate natural images. The VON not only generates images that are more realistic than state-of-the-art 2D image synthesis methods, but also enables many 3D operations such as changing the viewpoint of a generated image, editing of shape and texture, linear interpolation in texture and shape space, and transferring appearance across different objects and viewpoints.

关键词： Textures

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