Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing最新文献_第8页

Scalable clustering and applications 可伸缩集群和应用程序

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3010073

Shahid K I, S. Chaudhury

{"title":"Scalable clustering and applications","authors":"Shahid K I, S. Chaudhury","doi":"10.1145/3009977.3010073","DOIUrl":"https://doi.org/10.1145/3009977.3010073","url":null,"abstract":"Large scale machine learning is becoming an active research area recently. Most of the existing clustering algorithms cannot handle big data due to its high time and space complexity. Among the clustering algorithms, eigen vector based clustering, such as Spectral clustering, shows very good accuracy, but it has cubic time complexity. There are various methods proposed to reduce the time and space complexity for eigen decomposition such as Nyström method, Lanc-zos method etc. Nyström method has linear time complexity in terms of number of data points, but has cubic time complexity in terms of number of sampling points. To reduce this, various Rank k approximation methods also proposed, but which are less efficient compare to the normalized spectral clustering. In this paper we propose a two step algorithm for spectral clustering to reduce the time complexity toO(nmk + m2k'), by combining both Nyström and Lanczos method, where k is the number of clusters and k' is the rank k approximation of the sampling matrix (k < k' << m << n). It shows very good results, with various data sets, image segmentation problems and churn prediction of a telecommunication data set, even with very low sampling (for 10 Million × 10 Million matrix, sampled only 100 columns) with lesser time, which confirms the validity of the algorithm.","PeriodicalId":93806,"journal":{"name":"Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing","volume":"92 1","pages":"34:1-34:7"},"PeriodicalIF":0.0,"publicationDate":"2016-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90782060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Supervised deep segmentation network for brain extraction 脑提取的监督深度分割网络

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3010016

Apoorva Sikka, Gaurav Mittal, Deepti R. Bathula, N. C. Krishnan

{"title":"Supervised deep segmentation network for brain extraction","authors":"Apoorva Sikka, Gaurav Mittal, Deepti R. Bathula, N. C. Krishnan","doi":"10.1145/3009977.3010016","DOIUrl":"https://doi.org/10.1145/3009977.3010016","url":null,"abstract":"Recent past has seen an inexorable shift towards the use of deep learning techniques to solve a myriad of problems in the field of medical imaging. In this paper, a novel segmentation method involving a fully-connected deep neural network called Deep Segmentation Network (DSN) is proposed to perform supervised regression for brain extraction from T1-weighted magnetic resonance (MR) images. In contrast to the existing patch-based feature learning techniques, DSN works on full 3D volumes, simplifying pre- and post-processing operations, to efficiently provide a voxel-wise binary mask delineating the brain region. The model is evaluated using three publicly available datasets and is observed to either outdo or perform comparably to the state-of-the-art methods. DSN is able to achieve a maximum and minimum Dice Similarity Coefficient (DSC) of 97.57 and 92.82 respectively across all the datasets. Experiments conducted in this paper highlight the ability of the DSN model to automatically learn feature representations; making it a simple yet highly effective approach for brain segmentation. Preliminary experiments also suggest that the proposed model has the potential to segment sub-cortical structures accurately.","PeriodicalId":93806,"journal":{"name":"Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing","volume":"27 1","pages":"9:1-9:8"},"PeriodicalIF":0.0,"publicationDate":"2016-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90168050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Local dominant binary patterns for recognition of multi-view facial expressions 多视角面部表情识别的局部优势二值模式

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3010008

Bikash Santra, D. Mukherjee

{"title":"Local dominant binary patterns for recognition of multi-view facial expressions","authors":"Bikash Santra, D. Mukherjee","doi":"10.1145/3009977.3010008","DOIUrl":"https://doi.org/10.1145/3009977.3010008","url":null,"abstract":"In this paper, a novel framework is proposed for automatic recognition of facial expressions. However, the face images for the proposed problem are captured at multiple view angle (i.e., multi-view facial expressions). The proposed scheme introduces a local dominant binary pattern (LDBP). Unlike uniform LBP based features, the LDBP uses fewer feature dimension without affecting the recognition performances. The LDBP is computed by improvising LBP with dominant orientations of neighborhood pixels. The eigen-value analysis of structure tensor representation of expressive face images determines the dominant directions of gray value changes in local neighbors of pixels. We use SVM for view-specific classification of multi-view facial expressions. The proposed model is experimented with the benchmark datasets of both near-frontal (CK+ and JAFEE) and multi-view (KDEF, SFEW and LFPW) face images. The datasets include faces from posed as well as spontaneous expressions. The proposed scheme outperforms state-of-the-arts by approximately 1% for the near-frontal facial expressions and by at least 3% for multi-view facial expressions on an average.","PeriodicalId":93806,"journal":{"name":"Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing","volume":"47 1","pages":"25:1-25:8"},"PeriodicalIF":0.0,"publicationDate":"2016-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81421646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Feature-preserving 3D fluorescence image sequence denoising 保持特征的三维荧光图像序列去噪

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3009983

H. Bhujle

{"title":"Feature-preserving 3D fluorescence image sequence denoising","authors":"H. Bhujle","doi":"10.1145/3009977.3009983","DOIUrl":"https://doi.org/10.1145/3009977.3009983","url":null,"abstract":"In this paper feature-preserving denoising scheme for fluorescence video microscopy is presented. Fluorescence image sequences comprise of edges and fine structures with fast moving objects. Improving signal to noise ratio (SNR) while preserving structural details is a difficult task for these image sequences. Few existing denoising techniques result in over-smoothing these image sequences while others fail due to inappropriate implementation of motion estimation and compensation steps. In this paper we use nonlocal means (NLM) video denoising algorithm as to avoid motion estimation and compensation steps. The proposed shot boundary detection technique pre-processes the sequence systematically and accurately to form different shots with content-wise similar frames. To preserve the edges and fine structural details in the image sequences we modify the weighing term of NLM filter. Further, to accelerate the denoising process, separable non-local means filter is implemented for video sequences. We compare the results with existing fluorescence video de-noising techniques and show that the proposed method not only preserves the edges and small structural details more efficiently, also reduces the computational time. Efficacy of the proposed algorithm is evaluated quantitatively and qualitatively with PSNR and vision perception.","PeriodicalId":93806,"journal":{"name":"Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing","volume":"23 1","pages":"45:1-45:8"},"PeriodicalIF":0.0,"publicationDate":"2016-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82808377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Evaluation of graph layout methods based on visual perception 基于视觉感知的图形布局方法评价

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3010070

Jiafan Li, Yuhua Liu, Changbo Wang

引用次数: 7

Unsupervised domain adaptation without source domain training samples: a maximum margin clustering based approach 无源域训练样本的无监督域自适应:基于最大边际聚类的方法

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3010033

Sudipan Saha, Biplab Banerjee, S. Merchant

{"title":"Unsupervised domain adaptation without source domain training samples: a maximum margin clustering based approach","authors":"Sudipan Saha, Biplab Banerjee, S. Merchant","doi":"10.1145/3009977.3010033","DOIUrl":"https://doi.org/10.1145/3009977.3010033","url":null,"abstract":"Unsupervised domain adaptation (DA) techniques inherently assume the presence of ample amount of source domain training samples in addition to the target domain test data. The domains are characterized by domain-specific probability distributions governing the data which are substantially different from each other. The goal is to build a task oriented classifier model that performs proportionately in both the domains. In contrary to the standard unsupervised DA setup, we propose a maximum-margin clustering (MMC) based framework for the same which does not consider source domain labeled samples. Instead we formulate it as a joint clustering problem of all the samples from both the domains in a common feature subspace. The Geodesic Flow Kernel (GFK) based subspace projection technique in the Grassmannian manifold is adopted to cast the samples in a domain invariant space. Further, the MMC stage is followed to simultaneously group the data based on the maximization of margins and a classifier is learned for each group. The data overlapping problem is taken care of by specifically learning a SVM-KNN classifier for the potentially unreliable samples per group. We validate the framework on a pair of remote sensing images of different modalities for the purpose of land-cover classification and a generic object dataset for recognition. We observe that the proposed method exhibits performances at par with the fully supervised case for both the tasks but without the requirement of costly annotations.","PeriodicalId":93806,"journal":{"name":"Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing","volume":"46 1","pages":"56:1-56:8"},"PeriodicalIF":0.0,"publicationDate":"2016-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84889285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Sketch-based simulated draping for Indian garments 基于草图的印度服装模拟垂饰

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3010001

Sanjeev Muralikrishnan, P. Chaudhuri

引用次数: 3

A differential excitation based rotational invariance for convolutional neural networks 基于微分激励的卷积神经网络旋转不变性

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3009978

Haribabu Kandi, Deepak Mishra, G. R. S. Subrahmanyam

{"title":"A differential excitation based rotational invariance for convolutional neural networks","authors":"Haribabu Kandi, Deepak Mishra, G. R. S. Subrahmanyam","doi":"10.1145/3009977.3009978","DOIUrl":"https://doi.org/10.1145/3009977.3009978","url":null,"abstract":"Deep Learning (DL) methods extract complex set of features using architectures containing hierarchical set of layers. The features so learned have high discriminative power and thus represents the input to the network in the most efficient manner. Convolutional Neural Networks (CNN) are one of the deep learning architectures, extracts structural features with little invariance to smaller translational, scaling and other forms of distortions. In this paper, the learning capabilities of CNN's are explored towards providing improvement in rotational invariance to its architecture. We propose a new CNN architecture with an additional layer formed by differential excitation against distance for the improvement of rotational invariance and is called as RICNN. Moreover, we show that the proposed method is giving superior performance towards invariance to rotations against the original CNN architecture (training samples with different orientations are not considered) without disturbing the invariance to smaller translational, scaling and other forms of distortions. Different profiles like training time, testing time and accuracies are evaluated at different percentages of training data for comparing the performance of the proposed configuration with original configuration.","PeriodicalId":93806,"journal":{"name":"Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing","volume":"9 1","pages":"70:1-70:8"},"PeriodicalIF":0.0,"publicationDate":"2016-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85513191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Event geo-localization and tracking from crowd-sourced video metadata 从众包视频元数据中进行事件地理定位和跟踪

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3009993

Amit More, S. Chaudhuri

引用次数: 4

On the (soccer) ball 在足球上

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3010022

Samriddha Sanyal, A. Kundu, D. Mukherjee

引用次数: 9