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TSNet: Deep Network for Human Action recognition in Hazy Videos

TSNet: Deep Network for Human Action Recognition in Hazy Vid...

引用

2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018

作者： Chaudhary, Sachin Murala, Subrahmanyam Computer Vision and Pattern Recognition Lab Depatment of Electrical Engineering Indian Institute of Technology Ropar India

ISBN: (纸本)9781538666500

The all-weather intelligent surveillance system is the prime challenge for computer vision researchers. The surveillance is mostly done to analyze the human activity in a particular region. Several extreme weather conditions like rain, snow, haze, fog etc. halts the surveillance process and thus decreases the reliability of the surveillance system. Here, an attempt is made to tackle one of these weather situation i.e. haze in case of surveillance. Haze distorts the quality of images and videos captured by camera. Due to poor quality, it is difficult to analyze the haze degraded video for the human activities using the existing state-of-the-art methods for human action recognition (HAR). Therefore, in this paper, a new two level saliency based end-to-end network (TSNet) for HAR in hazy videos is proposed. De-hazing approaches given in [1]-[9] have certain limitations and therefore we fine-tuned the de-hazing network given in [10] for HAR. The concept of rank pooling given in [11] is further utilized to efficiently represent the temporal saliency of the video. The transmission map information is utilized here to fix the spatial saliency in each frame. As currently, there is no dataset availab.e for HAR in hazy video, here a new dataset of hazy video is generated from two benchmark datasets namely HMDB51 [12] and UCF101 [13] by adding synthetic haze. The existing methods for HAR proposed in [11], [14], [15] are applied and compared with the proposed method on proposed hazy-HMDB51 and hazy-UCF101. The proposed method clearly outperforms the above mentioned methods in terms of average recognition rate (ARR). © 2018 IEEE.

关键词： Monitoring

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Mash up of breaking news and contextual web information: A novel service for connected television

Mash up of breaking news and contextual web information: A n...

引用

2010 19th International Conference on computer Communications and Networks, ICCCN 2010

作者： Chattopadhyay, T. Pal, Arpan Garain, Utpal Innovation Lab. Kolkata Tata Consultancy Services Kolkata India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9781424471164

The Connected TV can be described as an Internet enabled TV. In the current paper we have proposed a system for connected TV that mash up the information from internet and RSS feeds related to the breaking news aired over the TV. The proposed system initially localize the text regions from the streamed video in hybrid mode, then recognize them and spot the key words and finally fetch the related information from Internet. Our experimental results show that the localization of the text regions from the video can work with no misses but have some false positives which are taken care by data semantic analysis. The errors of the Optical Character recognition (OCR) module are taken care by using string comparing techniques like longest common subsequence matching and Leveinsthein distance while matching with the RSS feed or internet. © 2010 IEEE.

关键词： Optical character recognition

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An ontological service oriented and web searching mechanism in distributed e-learning repositories

An ontological service oriented and web searching mechanism ...

引用

6th International Conference on Advanced Learning Technologies, ICALT 2006

作者： Votis, Konstantinos Alexakos, Christos Giotopoulos, Kostas Likothanassis, Spiridon Pattern Recognition Lab. Dept. of Computer Engineering and Informatics University of Patras Greece

ISBN: (纸本)0769526322

The great heterogeneity of web based Learning systems storing and providing digital e-learning data requires the introduction of interoperability aspects in order to resolve integration problems in a flexible and dynamic way. Learning Objects developed and stored in many different places on the web have a tremendous potential to benefit e-learning in particular and education in general. So the distributed e-learning data integration is significant for the enforcement of novel searching mechanisms in distributed e-learning repositories Our approach introduces an advanced search mechanism which initializes a semantic model representing the digital content stored in distributed e-learning servers, through the use of ontologies In order to retrieve useful content to be added to the learning environment while bypassing compatibility problems between different ontological representations of the same domain, we will use a mixed approach constituting both keywords and ontologies Searching tasks are carried out in the metadata level, where information concerning web digital content is published, managed and stored in the form of a scalab.e description of knowledge domains. © 2006 IEEE.

关键词： Ontology

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CROSS-MODAL SUBSPACE LEARNING FOR SKETCH-BASED IMAGE RETRIEVAL:A COMPARATIVE STUDY 5

CROSS-MODAL SUBSPACE LEARNING FOR SKETCH-BASED IMAGE RETRIEV...

引用

2016 5th IEEE International Conference on Network Infrastructure and Digital Content(IC-NIDC 2016)

作者： Peng Xu Ke Li Zhanyu Ma Yi-Zhe Song Liang Wang Jun Guo Pattern Recognition and Intelligent System Lab. Beijing University of Posts and Telecommunications SketchX Lab School of Electronic Engineering and Computer ScienceQueen Mary University of London National Lab of Pattern Recognition Institute of AutomationChinese Academy of Sciences

ISBN: (纸本)9781509012473

Sketch-based image retrieval（SBIR）has become a prominent research topic in recent years due to the proliferation of touch *** problem is however very challenging for that photos and sketches are inherently modeled in different *** are accurate（colored and textured）depictions of the real-world,whereas sketches are highly abstract（black and white）renderings often drawn from human *** naturally motivates us to study the effectiveness of various cross-modal retrieval methods in ***,to the best of our knowledge,all established cross-modal algorithms are designed to traverse the more conventional cross-modal gap of image and text,making their general applicableness to SBIR *** this paper,we design a series of experiments to clearly illustrate circumstances under which cross-modal methods can be best utilized to solve the SBIR *** specifically,we choose six state-of-the-art cross-modal subspace learning approaches that were shown to work well on image-text and conduct extensive experiments on a recently released SBIR ***,we present detailed comparative analysis of the experimental results and offer insights to benefit future research.

关键词： SBIR Cross-modal Subspace learning Comparison

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Region-based image annotation using asymmetrical support vector machine-based multiple-instance learning

Region-based image annotation using asymmetrical support vec...

引用

2006 IEEE computer Society Conference on computer vision and pattern recognition, CVPR 2006

作者： Yang, Changbo Dong, Ming Hua, Jing Machine Vision and Pattern Recognition Lab. Department of Computer Science Wayne State University Detroit MI 48202 Department of Computer Science Wayne State University Detroit MI 48202

ISBN: (纸本)0769525970

In region-based image annotation, keywords are usually associated with images instead of individual regions in the training data set. This poses a major challenge for any learning strategy. In this paper, we formulate image annotation as a supervised learning problem under Multiple-Instance Learning (MIL) framework. We present a novel Asymmetrical Support Vector Machine-based MIL algorithm (ASVM-MIL), which extends the conventional Support Vector Machine (SVM) to the MIL setting by introducing asymmetrical loss functions for false positives and false negatives. The proposed ASVM-MIL algorithm is evaluated on both image annotation data sets and the benchmark MUSK data sets. © 2006 IEEE.

关键词： Image processing

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Comparing and integrating alignment template and standard phrase-based statistical machine translation

Comparing and integrating alignment template and standard ph...

引用

8th International Conference on Intelligent Text Processing and Computational Linguistics

作者： Xu, Lin Cao, Xiaoguang Zhang, Bufeng Li, Mu Lab. of Pattern Recognition and Intelligent System Image Processing Center BeiHang University China Al Lab. Computer Science and Technology Tianjing University China Microsoft Research Asia China

ISBN: (纸本)9783540709381

In statistical machine translation (SMT) research, phrase-based methods have been receiving more interest in recent years. In this paper, we first give a brief survey of phrase-based SMT framework, and then make detailed comparisons of two typical implementations: alignment template approach and standard phrase-based approach. At last, we propose an improved model to integrate alignment template into standard phrase-based SMT as a new feature in a log-linear model. Experimental results show that our method outperforms the baseline method.

关键词： Natural language processing systems

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Clustering Web images with multi-modal features

Clustering Web images with multi-modal features

引用

15th ACM International Conference on Multimedia, MM'07

作者： Rege, Manjeet Dong, Ming Hua, Jing Machine Vision Pattern Recognition Lab. Department of Computer Science Wayne State University Detroit MI 48202 United States Graphics and Imaging Lab. Department of Computer Science Wayne State University Detroit MI 48202 United States

ISBN: (纸本)9781595937025

Web image clustering has drawn significant attention in the research community recently. However, not much work has been done in using multi-modal information for clustering Web images. In this paper, we address the problem of Web image clustering by simultaneous integration of visual and textual features from a graph partitioning perspective. In particular, we modelled visual features, images, and words from the surrounding text of the images using a tripartite graph. This graph is actually considered as a fusion of two bipartite graphs that are partitioned simultaneously by the proposed Consistent Isoperimetric High-order Co-clustering(CIHC) framework. Although a similar approach has been adopted before, the main contribution of this work lies in the computational efficiency, quality in Web image clustering and scalab.lity to large image repositories that CIHC is able to achieve. We demonstrate this through experimental results performed on real Web images. Copyright 2007 ACM.

关键词： Image analysis

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Segmentation of building facade domes

Segmentation of building facade domes

引用

17th Iberoamerican Congress on Progress in pattern recognition, Image Analysis, computer vision, and Applications, CIARP 2012

作者： Shalunts, Gayane Haxhimusa, Yll Sablatnig, Robert Vienna University of Technology Computer Vision Lab. Austria Vienna University of Technology Pattern Recongition and Image Processing Lab. Austria

ISBN: (纸本)9783642332746

Domes are architectural structural elements typical for ecclesiastical and secular grand buildings, like churches, mosques, palaces, capitols and city halls. The current paper targets the problem of segmentation of domes within the framework of architectural style classification of building facades. We perform segmentation of building facade domes by combining bilateral symmetry detection, graph-based segmentation approaches and image analysis and processing technics into a single method. Our algorithm achieves good segmentation results on buildings belonging to variety of architectural styles, such as Renaissanse, Neo-Renaissance, Baroque, Neo-Baroque, Neoclassical and Islamic. © 2012 Springer-Verlag.

关键词： Domes

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Graph theoretical framework for simultaneously integrating visual and textual features for efficient web image clustering 08

Graph theoretical framework for simultaneously integrating v...

引用

17th International Conference on World Wide Web 2008, WWW'08

作者： Rege, Manjeet Dong, Ming Hua, Jing Machine Vision Pattern Recognition Lab. Department ot Computer Science Wayne State University Detroit MI 48202 United States Graphics and Imaging Lab. Department ot Computer Science Wayne State University Detroit MI 48202 United States

ISBN: (纸本)9781605580852

With the explosive growth of Web and the recent development in digital media technology, the number of images on the Web has grown tremendously. Consequently, Web image clustering has emerged as an important application. Some of the initial efforts along this direction revolved around clustering Web images based on the visual features of images or textual features by making use of the text surrounding the images. However, not much work has been done in using multimodal information for clustering Web images. In this paper, we propose a graph theoretical framework for simultaneously integrating visual and textual features for efficient Web image clustering. Specifically, we model visual features, images and words from surrounding text using a tripartite graph. Partitioning this graph leads to clustering of the Web images. Although, graph partitioning approach has been adopted before, the main contribution of this work lies in a new algorithm that we propose - Consistent Isoperimetric High-order Co-clustering (CIHC), for partitioning the tripartite graph. Computationally, CIHC is very quick as it requires a simple solution to a sparse system of linear equations. Our theoretical analysis and extensive experiments performed on real Web images demonstrate the performance of CIHC in terms of the quality, efficiency and scalab.lity in partitioning the visual feature-image-word tripartite graph.

关键词： Digital storage

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Localized support vector machines for classification

Localized support vector machines for classification

引用

International Joint Conference on Neural Networks 2006, IJCNN '06

作者： Dong, Ming Wu, Jing Machine Vison and Pattern Recognition Lab. Department of Computer Science Wayne State University Detroit MI 48202 United States

ISBN: (纸本)0780394909

Support Vector Machince (SVMs) have been promising methods in pattern recognition becausc of their solid mathematical foundation. In this paper, we propuse a localized SVM classification scheme (LSVM), in which we first cluster the training data in each category, and then train a set of SVMs based on these clusters. The SVMs trained from the clusters in each category that are nearest to the given input psttern are then selected for the final classification. Our experiments on six UCI datasets show that LSVM outperforms the traditional SVM. © 2006 IEEE.

关键词： Support vector machines

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