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检索条件"任意字段=International Conference on Computer Vision and Image Processing, CVIP 2016"

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image DESCRIPTION GENERATION BY MODELING THE RELATIONSHIP BETWEEN OBJECTS 15

IMAGE DESCRIPTION GENERATION BY MODELING THE RELATIONSHIP BE...

引用

international conference on Wavelet Analysis and Pattern Recognition (ICWAPR)

作者： Bai, Lin Yang, Lina Huo, Lin Li, Taoshen Guangxi Univ Sch Comp Elect & Informat Nanning Peoples R China Guangxi Coll & Univ Key Lab Parallel & Distribute Nanning Peoples R China Guangxi Univ China ASEAN Res Inst Nanning Peoples R China

ISBN: (纸本)9781538652183

Automatically describing the content of an image is a challenging task in computer vision that connects the machine learning and natural language processing. In this paper, we present a framework, based on modeling image context, to generate natural sentences describing an image, which consists of two parts: relation modeling and description generating. By modeling the mapping from image spatial context to the logical relationship between objects, the former is trained to maximize the likelihood of the target linguistics phrase describing the relationship between object given the training image. By taking the the advantages of the syntactic-tree based method, the latter takes the predicted relationships as key ingredients to facilitate the image description generation within tree-growth process. We conduct extensive experimental evaluations on MS COCO dataset. Our framework outperforms the state-of-the-art methods. The results demonstrates that our framework provides robust and significant improvements for the relationship prediction between objects and the image description generation.

关键词： image description generation Relationship between objects Tree based approach image spatial context

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A Textural Characterization of Coal SEM images Using Functional Link Artificial Neural Network

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1st international conference on computer vision and image processing (cvip)

作者： Alpana Mohapatra, Subrajeet Birla Inst Technol Dept Comp Sci & Engn Ranchi 835215 Jharkhand India

ISBN: (纸本)9789811021046;9789811021039

In an absolute characterization trial, there are no substitutes for the final subtyping of coal quality independent of chemical analysis. Petrology is a specialty that deals with the understanding of the essential characteristics of the coal through appropriate chemical, morphological, or porosity analysis. Conventional analysis of coal by a petrologists is subjected to various shortcomings like inter-observer variations during screen analysis and due to different machine utilization, time consuming, highly skilled operator experience, and tiredness. In chemical analysis, use of conventional analyzers is expensive for characterization process. Thus, image analysis serves as an impressive automated characterization procedure of subtyping the coal, according to their textural, morphological, color, etc., features. Coal characterization is necessary for the proper utilization of coal in the power generation, steel, and several manufacturing industries. Thus, in this paper, attempts are made to devise the methodology for an automated characterization and sub-classification of different grades of coal samples using image processing and computational intelligence techniques.

关键词： Coal characterization image processing Scanned electron microscopy Computational intelligence

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Adaptive Clustering SOFC image Segmentation Based on Particle Swarm Optimization

Adaptive Clustering SOFC Image Segmentation Based on Particl...

引用

2019 3rd international conference on Machine vision and Information Technology (CMVIT 2019)

作者： Xuefei Yang Xiaowei Fu Xi Li College of Computer Science and Technology Wuhan University of Science and TechnologyHubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System State Key Laboratory of Materials Processing and Die & Mould Technology Huazhong University of Science and Technology College of Automation Huazhong University of Science and Technology

Microstructural parameters are important for analyzing the chemistry and performance of solid oxide fuel cells（SOFCs）. Aiming at the YSZ/Ni anode optical microscopy（OM） image of SOFC, in this paper, particle swarm intelligent optimization algorithm is used to improve the fuzzy C-means clustering algorithm for image segmentation. Particle swarm optimization is used to adaptively search the initial clustering center, helping to avoid local optimization and preserve more image detail. The experimental results show that the proposed method can improve the segmentation accuracy of images. At the same time, it can accurately segment the SOFC three-phase and provide effective image segmentation results for the microstructure parameters.

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A Novel Visual Word Assignment Model for Content-Based image Retrieval

引用

1st international conference on computer vision and image processing (cvip)

作者： Mukherjee, Anindita Chakraborty, Soman Sil, Jaya Chowdhury, Ananda S. Dream Inst Technol Kolkata India Jadavpur Univ Kolkata India IIEST Sibpur Howrah India

ISBN: (纸本)9789811021046;9789811021039

Visual bag of words model have been applied in the recent past for the purpose of content-based image retrieval. In this paper, we propose a novel assignment model of visual words for representing an image patch. In particular, a vector is used to represent an image patch with its elements denoting the affinities of the patch to belong to a set of closest/most influential visual words. We also introduce a dissimilarity measure, consisting of two terms, for comparing a pair of image patches. The first term captures the difference in affinities of the patches to belong to the common set of influential visual words. The second term checks the number of visual words which influences only one of the two patches and penalizes the measure accordingly. Experimental results on the publicly available COIL-100 image database clearly demonstrates the superior performance of the proposed content-based image retrieval (CBIR) method over some similar existing approaches.

关键词： Visual words Assignment model Dissimilarity measure image retrieval

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A Fast Local image Descriptor Based on Patch Quantization 1

引用

3rd international conference on Human Centered Computing (HCC)

作者： Tian, Tian Yang, Fan Zheng, Kun Yao, Hong Gao, Qian China Univ Geosci Coll Comp Sci 388 Lumo Rd Wuhan 430074 Hubei Peoples R China China Univ Geosci Fac Informat Engn 388 Lumo Rd Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Sch Math & Stat 1037 Luoyu Rd Wuhan 430074 Hubei Peoples R China

ISBN: (数字)9783319745213

ISBN: (纸本)9783319745213;9783319745206

Representing local image patches is a key step in many applications of computer vision, while fast and effective description methods are always required by real-time image processing. Motivated by the fact that quantization compresses information while preserving primary structures, in this paper, we propose to use vector quantization (VQ) on local patch descriptor building. Compared to conventional approaches that compress floating-point features with VQ, we produce local integer descriptors very fast directly based on simple quantization methods. Experimental results on a publicly available dataset show that the present method is efficient both to build and to match. It achieves comparable performance to some typical floating-point and binary descriptors such as SIFT and BRIEF, offering a novel solution to fast local image representation except for bit test created in BRIEF.

关键词： Local image descriptor Vector quantization Feature matching Cumulative distribution function

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Performance Analysis of Texture image Retrieval in Curvelet, Contourlet, and Local Ternary Pattern Using DNN and ELM Classifiers for MRI Brain Tumor images

引用

1st international conference on computer vision and image processing (cvip)

作者： Pandian, A. Anbarasa Balasubramanian, R. Manonmaniam Sundaranar Univ Dept Comp Sci & Engn Tirunelveli India

ISBN: (纸本)9789811021046;9789811021039

The problem of searching a digital image in a very huge database is called content-based image retrieval (CBIR). Texture represents spatial or statistical repetition in pixel intensity and orientation. When abnormal cells form within the brain is called brain tumor. In this paper, we have developed a texture feature extraction of MRI brain tumor image retrieval. There are two parts, namely feature extraction process and classification. First, the texture features are extracted using techniques like curvelet transform, contourlet transform, and Local Ternary Pattern (LTP). Second, the supervised learning algorithms like Deep Neural Network (DNN) and Extreme Learning Machine (ELM) are used to classify the brain tumor images. The experiment is performed on a collection of 1000 brain tumor images with different modalities and orientations. Experimental results reveal that contourlet transform technique provides better than curvelet transform and local ternary pattern.

关键词： CBIR Texture Brain tumor DNN and ELM

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Applicability of Self-Organizing Maps in Content-Based image Classification

引用

1st international conference on computer vision and image processing (cvip)

作者： Rohit, Kumar Gorthi, R. K. Sai Subrahmanyam Mishra, Deepak Natl Inst Technol Hamirpur 177005 Himachal Prades India Indian Inst Space Sci & Technol Thiruvananthapuram 695547 Kerala India

ISBN: (纸本)9789811021046;9789811021039

image databases are getting larger and diverse with the coming up of new imaging devices and advancements in technology. Content-based image classification (CBIC) is a method to classify images from large databases into different categories, on the basis of image content. An efficient image representation is an important component of a CBIC system. In this paper, we demonstrate that Self-Organizing Maps (SOM)-based clustering can be used to form an efficient representation of an image for a CBIC system. The proposed method first extracts Scale-Invariant Feature Transform (SIFT) features from images. Then it uses SOM for clustering of descriptors and forming a Bag of Features (BOF) or Vector of Locally Aggregated Descriptors (VLAD) representation of image. The performance of proposed method has been compared with systems using k-means clustering for forming VLAD or BOF representations of an image. The classification performance of proposed method is found to be better in terms of F-measure (FM) value and execution time.

关键词： Content-Based image Classification (CBIC) Self-Organizing Maps (SOM) Scale-Invariant Feature Transform (SIFT) Bag of Features (BOF) Vector of Locally Aggregated Descriptors (VLAD)

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Gait Recognition-Based Human Identification and Gender Classification

引用

1st international conference on computer vision and image processing (cvip)

作者： Arivazhagan, S. Induja, P. Mepco Schlenk Engn Coll Dept ECE Sivakasi India

ISBN: (纸本)9789811021046;9789811021039

The main objective of this work is to identify the persons and to classify the gender of those persons with the help of their walking styles from the gait sequences with arbitrary walking directions. The human silhouettes are extracted from the given gait sequences using background subtraction technique. Median value approach is used for the background subtraction. After the extraction of the silhouettes, the affinity propagation clustering is performed to group the silhouettes with similar views and poses to one cluster. The cluster-based averaged gait image is taken as a feature for each cluster. To learn the distance metric, sparse reconstruction-based metric learning has been used. It minimizes the intraclass sparse reconstruction errors and maximizes the interclass reconstruction errors simultaneously. The above-mentioned steps have come under the training phase. With the help of the metric learned in the training and the feature extracted from the testing video sequence, sparse reconstruction-based classification has been performed for identifying the person and gender classification of that person. The accuracy achieved for the human identification and gender classification is promising.

关键词： Affinity propagation (AP) Cluster-based averaged gait image (C-AGI) Sparse reconstruction-based metric learning (SRML) And sparse reconstruction-based classification (SRC)

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Robust image Hashing Technique for Content Authentication based on DWT

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1st international conference on computer vision and image processing (cvip)

作者： Vadlamudi, Lokanadham Naidu Vaddella, Rama Prasad V. Devara, Vasumathi Sree Vidyanikethan Engn Coll Informat Technol Tirupati 517102 AP India Sree Vidyanikethan Engn Coll Comp Sci & Engn Tirupati 517102 AP India JNT Univ JNTU Coll Engn Comp Sci & Engn Hyderabad 500085 TS India

ISBN: (纸本)9789811021046;9789811021039

This paper presents an image hashing technique for content verification using Discrete Wavelet Transform (DWT) approximation features. The proposed technique converts resized RGB color images to L*a*b* color images. Further, images are regularized using Gaussian low pass filter. A level 2, 2D DWT is applied on L* component of L*a*b* color image and the LL2 approximation sub-band image is chosen for feature extraction. The features are extracted by utilizing a sequence of circles on approximation sub-band image. Finally, the robust binary hash is generated from extracted features. The experimental results indicate that the hash of the presented technique is invariant to standard content preserving manipulations and malicious content altering operations. The experiment results of Receiver Operating Characteristics (ROC) plots indicate that the presented technique shows strong discriminative and robustness capabilities. Besides, the hash of the proposed technique is shorter in length and key dependent.

关键词： image authentication image hashing Discrete Wavelet Transform Approximation features Robustness

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Improved Local Texture Features for Pedestrian Detection 3

Improved Local Texture Features for Pedestrian Detection

引用

3rd IEEE international conference on image, vision and Computing, ICIVC 2018

作者： Ling, Xiao Yongjun, Zhang Qian, Wang Yuewei, Li College of Computer Science and Technology Key Lab. of Intelligent Med. Image Anal. and Precise Diagn. of Guizhou Province Guizhou University Guiyang China

ISBN: (纸本)9781538649916

Pedestrian detection is a hot issue in the field of computer vision and image processing in recent years. It has important application value in the domain of unmanned cars and driver assistance systems and so on, but there are existed many problems that need to be solved. In this paper, we present an improved texture feature MLBP (Mean of Local Binary Pattern) and the CMLBP (Color based on Mean of Local Binary Pattern) feature based on various color spaces. When the uniform LBP feature does not consider the influence of noise, the mutation of central pixel and neighborhood pixel is not taken into account and therefore the extraction processes of MLBP feature improve the calculation method of the uniform LBP, which makes the extracted feature more stable. The MLBP feature is extracted from gray images, yet color images transformed into gray images generally loss a great amount of information. In view of this point, we also propose the CMLBP feature based on multiple color spaces that is a more comprehensive description of the texture feature of images. In the INRIA pedestrian dataset, many experiments have been conducted with SVM and HIKSVM classifier, and the results manifest that the detection rates of MLBP and CMLBP are much better than the uniform LBP and the basic LBP. The combination of MLBP, CMLBP and other features has been applied to pedestrian detection, which also achieves good results. © 2018 IEEE.

关键词： Local binary pattern

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