Proceedings of the Third International Symposium on Computer Vision and the Internet最新文献

Selfie Search: Image Retrieval and Face Recognition in iOS: [Invited Paper] 自拍照搜索:iOS中的图像检索和人脸识别:[特邀论文]

Proceedings of the Third International Symposium on Computer Vision and the Internet Pub Date : 2016-09-21 DOI: 10.1145/2983402.2983420

Oge Marques, Joseph Carson

引用次数: 0

Spam and Sentiment Analysis Model for Twitter Data using Statistical Learning 使用统计学习的Twitter数据垃圾邮件和情感分析模型

Proceedings of the Third International Symposium on Computer Vision and the Internet Pub Date : 2016-09-21 DOI: 10.1145/2983402.2983404

Anita, D. Gupta, Ashish Kumar

引用次数: 1

Automatic Blood Cell Segmentation Using K-Mean Clustering from Microscopic Thin Blood Images 基于k均值聚类的显微薄血图像自动血细胞分割

Proceedings of the Third International Symposium on Computer Vision and the Internet Pub Date : 2016-09-21 DOI: 10.1145/2983402.2983409

S. S. Savkare, A. S. Narote, S. P. Narote

引用次数: 16

An Optimal Approach for Web Service Selection Web服务选择的最优方法

Proceedings of the Third International Symposium on Computer Vision and the Internet Pub Date : 2016-09-21 DOI: 10.1145/2983402.2983436

Pal Bagtharia, M. H. Bohra

引用次数: 4

A Novel Methodology for Vehicle Number Plate Recognition using Artificial Neural Network 一种基于人工神经网络的车牌识别方法

Proceedings of the Third International Symposium on Computer Vision and the Internet Pub Date : 2016-09-21 DOI: 10.1145/2983402.2983432

Sangita Kumari, D. K. Gupta, Rajdeep Singh

引用次数: 4

Eigenvalue Analysis with 2D-DCT and BBP for Shape Representation and Classification 基于2D-DCT和BBP的形状表示与分类特征值分析

Proceedings of the Third International Symposium on Computer Vision and the Internet Pub Date : 2016-09-21 DOI: 10.1145/2983402.2983414

Bharathi Pilar, B. H. Shekar

{"title":"Eigenvalue Analysis with 2D-DCT and BBP for Shape Representation and Classification","authors":"Bharathi Pilar, B. H. Shekar","doi":"10.1145/2983402.2983414","DOIUrl":"https://doi.org/10.1145/2983402.2983414","url":null,"abstract":"In this work, we present eigenvalue based shape descriptor which makes use of small eigenvalue and large eigenvalue along with two dimensional Discrete Cosine Transformation (2D-DCT) for the purpose of feature extraction. The DCT based features are combined with Block based Binary Pattern (BBP) and hence propose the combined classifier model for shape representation and classification. The small eigenvalue and large eigenvalue are computed for each pixel associated with a shape, capturing the structure of a shape. It is well known fact that the 2D-DCT is capable of capturing the region information and does the energy compaction. Hence, we perform 2D-DCT on these two eigenvalue based matrices to obtain compact representation of the shape and are matched using Euclidean Distance. We have also proposed a variant of local binary pattern called blockwise binary pattern (BBP) which is found to be invariant to rotation and shift of the object. The histogram features obtained due to proposed BBP are matched using Earth Movers Distance (EMD) metric. Finally, to improve the classification accuracy, we have proposed a decision level fusion strategy which integrates 2D-DCT based features with BBP. Extensive experimental results on the publicly available shape databases namely, Kimia-99 and Kimia-216 and MPEG-7 data sets demonstrate the accuracy of the proposed method and comparative analysis exhibit that the proposed approach classifies more accurately than many baseline shape matching algorithms.","PeriodicalId":283626,"journal":{"name":"Proceedings of the Third International Symposium on Computer Vision and the Internet","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126504513","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

Computing Correlative Association of Terms for Automatic Classification of Text Documents 用于文本文档自动分类的术语关联计算

Proceedings of the Third International Symposium on Computer Vision and the Internet Pub Date : 2016-09-21 DOI: 10.1145/2983402.2983424

Deepak Agnihotri, K. Verma, Priyanka Tripathi

{"title":"Computing Correlative Association of Terms for Automatic Classification of Text Documents","authors":"Deepak Agnihotri, K. Verma, Priyanka Tripathi","doi":"10.1145/2983402.2983424","DOIUrl":"https://doi.org/10.1145/2983402.2983424","url":null,"abstract":"The selection of most informative terms reduces the feature set and speed up the classification process. The most informative terms are highly affected by the correlative association of the terms. The rare terms are most informative than sparse and common terms. The main objective of this study is assigning a higher weight to the rare terms and less weight to the common and sparse terms. The terms weight are computed by giving emphasis on terms- strength, mutual information and strong association with the specific class. In this context, we proposed, a novel hybrid feature selection method named as, Correlative Association Score (CAS) of terms. The CAS utilizes the concept of Apriori algorithm to select the most informative terms. Initially, the CAS select most informative terms from the entire extracted terms. Subsequently, the N-grams of range (1,3) are generated from these informative terms. Finally, the standard Chi Square (χ2) method is applied to select most informative N-grams. The two standard classifiers Multinomial Naive Bayes (MNB) and Linear Support Vector Machine (LSVM) are applied on four standard text data sets Webkb, 20Newsgroup, Ohsumed10, and Ohsumed23. The promising results of extensive experiments demonstrate the effectiveness of the CAS in compared to state-of-the-art methods viz. Mutual Information (MI), Information Gain (IG), Discriminating Feature Selection (DFS), and χ2.","PeriodicalId":283626,"journal":{"name":"Proceedings of the Third International Symposium on Computer Vision and the Internet","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130687486","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: 11

Detecting Video Shot Boundaries by Modified Tomography 基于改进断层扫描的视频镜头边界检测

Proceedings of the Third International Symposium on Computer Vision and the Internet Pub Date : 2016-09-21 DOI: 10.1145/2983402.2983441

Jina Varghese, K. N. Nair

引用次数: 3

RISA: Rotation Illumination Scale and Affine Invariant Face Recognition 旋转光照尺度与仿射不变人脸识别

Proceedings of the Third International Symposium on Computer Vision and the Internet Pub Date : 2016-09-21 DOI: 10.1145/2983402.2983415

A. Vinay, Vinay S. Shekhar, Gagana B., A. B, K. N. B. Murthy, S. Natarajan

{"title":"RISA: Rotation Illumination Scale and Affine Invariant Face Recognition","authors":"A. Vinay, Vinay S. Shekhar, Gagana B., A. B, K. N. B. Murthy, S. Natarajan","doi":"10.1145/2983402.2983415","DOIUrl":"https://doi.org/10.1145/2983402.2983415","url":null,"abstract":"Face Recognition (FR) has been on the forefront of research efforts for the past two decades. In spite of considerable strides, it still suffers from the curse of false matches in the presence of variations in terms of parameters such as affine, scale, rotation and illumination. Since, real world images inherently consists of such variations, an effective FR system, should handle such variations deftly. Hence, in this paper, we propose a robust, yet simple and cost effective technique for overcoming some of the aforementioned challenges. The first stage of the proposed system deals with illumination variations by performing logarithm transform on the input face images. Further, the Non-subsampled Contourlet Transform (NSCT) is used to decompose the logarithm transformed facial images into low frequency and high frequency components. Subsequently, histogram equalization is carried out on the low frequency components. Finally, we employ Affine Scale Invariant Feature Transform (ASIFT) to find corresponding points that are translation and scale invariant. We will demonstrate by carrying out extensive experimentations on the benchmark datasets: ORL, Grimace, Face95 and Yale, that the proposed technique is more robust and yields comparable efficacy to most of the contemporary approaches.","PeriodicalId":283626,"journal":{"name":"Proceedings of the Third International Symposium on Computer Vision and the Internet","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122254626","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

Automatic Image Segmentation Algorithm for Microscopic Images of Liquorice and Rhubarb 甘草和大黄显微图像的自动分割算法

Proceedings of the Third International Symposium on Computer Vision and the Internet Pub Date : 2016-09-21 DOI: 10.1145/2983402.2983422

Shraddha Vyas, B. Fataniya, T. Zaveri, S. Acharya

{"title":"Automatic Image Segmentation Algorithm for Microscopic Images of Liquorice and Rhubarb","authors":"Shraddha Vyas, B. Fataniya, T. Zaveri, S. Acharya","doi":"10.1145/2983402.2983422","DOIUrl":"https://doi.org/10.1145/2983402.2983422","url":null,"abstract":"This paper proposes an automated algorithm for plant identification using microscopic images of powder of herbal plants. In current scenario, the task of identifying plant from its powder form is done by pharmaceutical companies, which perform this task manually. This process takes lots of effort and time. Microscopic image of powder contains varieties of information, which are important evidence for identification of the plant. With every image, different type of noise are present, which makes the segmentation as a critical job. In this paper, we are proposing an algorithm which performs this task automatically by a computer. Our method consists two steps: \"Pre-Processing\" and \"Image Segmentation\". Firstly, microscopic images of \"Liquorice\" and \"Rhubarb\" plants were taken. On those images Top-hat and Bot-hat transformation are performed. Wiener Filter is used for image smoothing. An image segmentation is performed using Otsu's thresholding algorithm and find region of interest. The extra blobs were removed using morphological operations. Our proposed algorithm shows the efficiency for successfully detection of Liquorice and Rhubarb plants are 91.37% and 92.94% respectively.","PeriodicalId":283626,"journal":{"name":"Proceedings of the Third International Symposium on Computer Vision and the Internet","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131157487","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