2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR)最新文献_第3页

Muzzle Analysis for Biometric Identification of Pigs 猪生物特征识别的口鼻分析

2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR) Pub Date : 2017-12-01 DOI: 10.1109/ICAPR.2017.8593204

K. Karthik, Shoubhik Chakraborty, S. Banik

引用次数: 1

Event Recognition in Unconstrained Video using Multi-Scale Deep Spatial Features 基于多尺度深度空间特征的无约束视频事件识别

2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR) Pub Date : 2017-12-01 DOI: 10.1109/ICAPR.2017.8592958

Saiyed Umer, Mrinmoy Ghorai, Partha Pratim Mohanta

引用次数: 2

Quartiles based UnderSampling(QUS): A Simple and Novel Method to increase the Classification rate of positives in Imbalanced Datasets 基于四分位的欠采样(QUS):一种提高不平衡数据集阳性分类率的简单新方法

2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR) Pub Date : 2017-12-01 DOI: 10.1109/ICAPR.2017.8593202

C. V. K. Veni, T. Rani

{"title":"Quartiles based UnderSampling(QUS): A Simple and Novel Method to increase the Classification rate of positives in Imbalanced Datasets","authors":"C. V. K. Veni, T. Rani","doi":"10.1109/ICAPR.2017.8593202","DOIUrl":"https://doi.org/10.1109/ICAPR.2017.8593202","url":null,"abstract":"The main challenge in learning from imbalanced datasets is the presence of a large set of training examples available for the negatives(majority class instances), and very few positives(minority class instances). This may result in a good overall performance of the classifier even though there is a huge red uction in the classification rate of positives. Quartiles based UnderSampling(QUS) method proposed in this paper, addresses the above problem in a simple way. That is balancing the dataset by selecting the negatives based on their similarity with respect to 5 quartiles: minimum, quartile1(Q1), median, quartile3(Q3) and maximum. Intention is to reduce the influence of excessive negatives on the classifier, which may bias it towards a better negatives classification otherwise. An advantage of this undersampling method is parameter independence and gives better results compared to the state of the art methods. The proposed method is tested on kNN (k Nearest Neighbour) classifier and empirical results improve the classification rate of positives than the original unprocessed imbalanced dataset.","PeriodicalId":239965,"journal":{"name":"2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132067569","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

What's that Style? A CNN-based Approach for Classification and Retrieval of Building Images 那是什么风格?基于cnn的建筑图像分类与检索方法

2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR) Pub Date : 2017-12-01 DOI: 10.1109/ICAPR.2017.8593206

Rachel D. Meltser, S. Banerji, A. Sinha

引用次数: 3

Context-based Sarcasm Detection in Hindi Tweets 基于上下文的印地语推文讽刺检测

2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR) Pub Date : 2017-12-01 DOI: 10.1109/ICAPR.2017.8593198

S. Bharti, Korra Sathya Babu, Rahul Raman

{"title":"Context-based Sarcasm Detection in Hindi Tweets","authors":"S. Bharti, Korra Sathya Babu, Rahul Raman","doi":"10.1109/ICAPR.2017.8593198","DOIUrl":"https://doi.org/10.1109/ICAPR.2017.8593198","url":null,"abstract":"Sentiment analysis is the way of finding ones' opinion towards any specific target. Sarcasm is a special type of sentiment which infers the opposite meaning of what people convey in the text. It is often expressed using positive or intensified positive words. Nowadays, posting sarcastic messages on social media like Twitter, Facebook, WhatsApp, etc., became a new trend to avoid direct negativity. In the presence of sarcasm, sentiment analysis on these social media texts became the most challenging task. Therefore, an automated system is required for sarcasm detector in textual data. Many researchers have proposed several sarcasm detection techniques to identify sarcastic text. These techniques are designed to detect sarcasm on the text scripted in English since it is the most popular language in social networking groups. However, parallel research for sarcasm detection on different Asian languages like Hindi, Telugu, Tamil, Urdu, and Bengali are not yet explored. One of the reasons for the less exploration of these languages for sarcastic sentiment analysis is the lack of annotated corpus even though they are popular in a large networked society. In this article, we proposed a context-based pattern i.e. “sarcasm as a contradiction between a tweet and the context of its related news” for sarcasm detection in Hindi tweets. The proposed approach utilized Hindi news as the context of a tweet with in the same timestamp and attained an accuracy of 87 %.","PeriodicalId":239965,"journal":{"name":"2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR)","volume":"255 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122078661","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: 15

Segmentation of Natural Image Based on Colour Cohesion and Spatial Criteria 基于色彩内聚和空间准则的自然图像分割

2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR) Pub Date : 2017-12-01 DOI: 10.1109/ICAPR.2017.8593106

Aritra Mukherjee, S. Sarkar, S. Saha

{"title":"Segmentation of Natural Image Based on Colour Cohesion and Spatial Criteria","authors":"Aritra Mukherjee, S. Sarkar, S. Saha","doi":"10.1109/ICAPR.2017.8593106","DOIUrl":"https://doi.org/10.1109/ICAPR.2017.8593106","url":null,"abstract":"Segmenting a natural image is a complex task. Different semantic units may share similar visual features. On the other hand, such features can have variations even within a single unit. Proposed methodology relies on colour cohesion and spatial relationship between the components with cohesive colour. At first image colour space is clustered to map the original colour to a reduced set. Number of cluster is automatically detected by analyzing the intensity histograms of the colour channels. Based on the similarity in terms of mapped colours, pixels are grouped. Subsequently, the spatial inclusiveness criteria is considered to merge the pixels groups where one group is contained within another. Finally, an attempt is made to merge the adjacent regions based on colour gradient. Colour cohesion is conceptualized by the process of colour space clustering, grouping of pixels in terms of colour similarity and region merging based on colour gradient. The spatial criteria is taken into account in terms of spatial inclusiveness at intermediate level and adjacency at final stage. Proposed methodology is tested on Berkley segmentation dataset. Performance comparison with few other methodologies indicates the effectiveness of proposed methodology.","PeriodicalId":239965,"journal":{"name":"2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129510881","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

Object Classification using Ensemble of Local and Deep Features 基于局部和深度特征集成的目标分类

2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR) Pub Date : 2017-12-01 DOI: 10.1109/ICAPR.2017.8593056

Siddharth Srivastava, Prerana Mukherjee, Brejesh Lall, Kamlesh Jaiswal

引用次数: 6

Image Inpainting using Geometric Transformations for Digital Circuit Images 利用几何变换对数字电路图像进行图像修补

2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR) Pub Date : 2017-12-01 DOI: 10.1109/ICAPR.2017.8593088

Ramanath Datta, Mrinmoy Ghorai, Sekhar Mandal

引用次数: 0

RGB Patch Clustering for Hyperspectral Image Super-resolution Using Sparse Coding 基于稀疏编码的高光谱图像超分辨率RGB Patch聚类

2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR) Pub Date : 2017-12-01 DOI: 10.1109/ICAPR.2017.8592997

V. G. Sreena, C. Jiji

{"title":"RGB Patch Clustering for Hyperspectral Image Super-resolution Using Sparse Coding","authors":"V. G. Sreena, C. Jiji","doi":"10.1109/ICAPR.2017.8592997","DOIUrl":"https://doi.org/10.1109/ICAPR.2017.8592997","url":null,"abstract":"Hyperspectral images (HSI's) are characterized by low spatial resolution and high spectral resolution. In this paper, we propose a method for improving the spatial resolution of HSI's making use of the high spatial resolution RGB image. We solve this problem under a sparse reconstruction framework with clustering of similar patches in the high resolution RGB image. To this end, we first learn a dictionary of atoms from the given low resolution HSI, followed by clustering of similar patches in the RGB image. Sparse coefficients corresponding to individual clusters are estimated using Matching Pursuit Algorithm. Since RGB image is considered to be the spectrally transformed version of desired high resolution HSI, we use the coefficients estimated as above for the reconstruction of high resolution HSI. Patchwise clustering ensures the spatial similarity between various patches in a cluster. Experimental results show that the proposed method effectively recovers the spatial details, preserving the spectral information and removes the block artifacts associated with an independent application of GSOMP on all patches. Our method gives better result compared to other state of the art techniques like MF, SASFM and GSOMP.","PeriodicalId":239965,"journal":{"name":"2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132117704","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

Study of Node Arrival in Evolution of Disease Network 疾病网络进化中的节点到达问题研究

2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR) Pub Date : 2017-12-01 DOI: 10.1109/ICAPR.2017.8593147

Rodda Monica, S. Bhavani, T. Rani

引用次数: 0