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Updates found with 'categorization'

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Updates found with 'categorization'

MATLAB PROJECTS ABSTRACT 2016-2017 WEAKLY SUPERVISED FINE-GRAINED CATEGORIZATION WITH PART-BASED IMAGE REPRESENTATION ABSTRACT: In this paper, we propose a fine-grained image categorization system with easy deployment. We do not use any object/part annotation (weakly-supervised) in the training or in the testing stage, but only class labels for training images. Fine-grained image categorization aims to classify objects with only subtle distinctions (e.g., two breeds of dogs that look alike). Most existing works heavily rely on object/part detectors to build the correspondence between object parts, which require accurate object or object part annotations at least for training images. The need for expensive object annotations prevents the wide usage of these methods. Instead, we propose to generate multistage part proposals from object proposals, select useful part proposals, and use them to compute a global image representation for categorization. This is specially designed for the weakly supervised fine-grained categorization task, because useful parts have been shown to play a critical role in existing annotation dependent works but accurate part detectors are hard to acquire. With the proposed image representation, we can further detect and visualize the key (most discriminative) parts in objects of different classes. In the experiments, the proposed weakly supervised method achieves comparable or better accuracy than state-of-the-art weakly-supervised methods and most existing annotation-dependent methods on three challenging datasets. Its success suggests that it is not always necessary to learn expensive object/part detectors in fine-grained image categorization.
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IEEE 2016 - 2017 Matlab Image Processing TitlesS.No Project Titles 1. Data-driven Soft Decoding of Compressed Images in Dual Transform-Pixel Domain 2. Double-Tip Arte fact Removal from Atomic Force Microscopy Images 3. Quaternion Collaborative and Sparse Representation With Application to Color Face Recognition 4. Multi-Level Canonical Correlation Analysis for Standard-Dose PET Image Estimation 5. Weakly Supervised Fine-Grained Categorization with Part-Based Image Representation 6. Robust Visual Tracking via Convolutional Networks without Training 7. Context-based prediction filtering of impulse noise images 8. Predicting the Forest Fire Using Image Processing 9. A Review Paper on detection of Glaucoma using Retinal Fundus Images 10. Performance Analysis of Filters on Complex Images for Text Extraction through Binarization 11. Automated Malaria Detection from Blood Samples Using Image Processing 12. Learning Invariant Color Features for Person Re-Identification 13. A Diffusion and Clustering-based Approach for Finding Coherent Motions and Understanding Crowd Scenes 14. Automatic Design of Color Filter Arrays in The Frequency Domain 15. Learning Iteration-wise Generalized Shrinkage-Thresholding Operators for Blind Deconvolution 16. Image Segmentation Using Parametric Contours With Free Endpoints 17. CASAIR: Content and Shape-Aware Image Retargeting and Its Applications 18. Texture classification using Dense Micro-block Difference 19. Statistical performance analysis of a fast super-resolution technique using noisy translations 20. Trees Leaves Extraction In Natural Images Based On Image segmentation and generating Its plant details
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IEEE 2016-2017 BIG DATA PROJECTS ABSTRACT A CROWDSOURCING WORKER QUALITY EVALUATION ALGORITHM ON MAPREDUCE FOR BIG DATA APPLICATIONSABSTRACT: Crowdsourcing is a new emerging distributed computing and business model on the backdrop of Internet blossoming. With the development of crowdsourcing systems, the data size of crowd sourcers, contractors and tasks grows rapidly. The worker quality evaluation based on big data analysis technology has become a critical challenge. This paper first proposes a general worker quality evaluation algorithm that is applied to any critical tasks such as tagging, matching, filtering, categorization and many other emerging applications, without wasting resources. Second, we realize the evaluation algorithm in the Had oop platform using the Map Reduce parallel programming model. Finally, to effectively verify the accuracy and the effectiveness of the algorithm in a wide variety of big data scenarios, we conduct a series of experiments. The experimental results demonstrate that the proposed algorithm is accurate and effective. It has high computing performance and horizontal scalability. And it is suitable for large-scale worker quality evaluation sin a big data environment. EXISTING SYSTEM: There are many problems in which one seeks to develop predictive models to map between a set of predictor variables and an outcome. Statistical tools such as multiple regression or neural networks provide mature methods for computing model parameters when the set of predictive covariates and the model structure are pre-specified. Furthermore, recent research is providing new tools for inferring the structural form of non-linear predictive models, given good input and output data . However, the task of choosing which potentially predictive variables to study is largely a qualitative task that requires substantial domain expertise. For example, a survey designer must have domain expertise to choose questions that will identify predictive covariates. An engineer must develop substantial familiarity with a design in order to determine which variables can be systematically adjusted in order to optimize performance. The need for the involvement of domain experts can become a bottleneck to new insights. However, if the wisdom of crowds could be harnessed to produce insight into difficult problems, one might see exponential rises in the discovery of the causal factors of behavioral outcomes, mirroring the exponential growth on other online collaborative communities. Thus, the goal of this research was to test an alternative approach to modeling in which the wisdom of crowds is harnessed to both propose potentially predictive variables to study by asking questions, and respond to those questions, in order to develop a predictive model. PROPOSED SYSTEM: This paper introduces, for the first time, a method by which non domain experts can be motivated to formulate independent variables as well as populate enough of these variables for successful modeling. In short, this is accomplished as follows. Users arrive at a website in which a behavioral outcome is to be modeled. Users provide their own outcome and then answer questions that may be predictive of that outcome. Periodically, models are constructed against the growing data sets that predict each user’s behavioral outcome. Users may also pose their own questions that, when answered by other users, become new independent variables in the modeling process. In essence, the task of discovering and populating predictive independent variables is outsourced to the user community. CONCLUSION: In this paper, we first proposed a general worker quality evaluation algorithm, which is applied to any critical crowdsourcing tasks without pre-developed answers. Then, to satisfy the demand of parallel evaluation for a multitude of workers in a bigdata environment, we im-plement the proposed algorithm in the Hadoop platform using the MapReduce programming model. The experi-mental results show that the algorithm is accurateand has high efficiency and performance in a big data environment. In our future studies, we will further consider other factors that affect worker quality, such as answer time and task difficulty. And these factors will help realize the comprehensive evaluation of worker quality to adapt the worker quality evaluation issue under different situations for the crowdsourcing mode in a big data environment.
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IEEE DOT NET PROJECT ABSTRACT 2016-2017 EDUCATIONAL DATA MINING: A REVIEW OF THE STATE OF THE ART ABSTRACT:- Educational data mining (EDM) is an emerging interdisciplinary research area that deals with the development of methods to explore data originating in an educational context. EDM uses computational approaches to analyze educational data in order to study educational questions. This paper surveys the most relevant studies carried out in this field to date. First, it introduces EDM and describes the different groups of user, types of educational environments, and the data they provide. It then goes on to list the most typical/common tasks in the educational environment that have been resolved through data-mining techniques, and finally, some of the most promising future lines of research are discussed.SYSTEM ANALYSIS:-EXISTING SYSTEM:-• Data sets they collect do not unproblematic model or mirror the world events.• Aggression level of the network as high or as low as desired.• The mass media in Twitter, unlike the traditional media networks.• Very limited prior work on the categorization of relevant and irrelevant question ranking capability that scores each question.• System detects earthquakes promptly and notification is delivered much faster than JMA broadcast announcements.• Focused only electrical engineering module titled Electronics, which serves as the case study.• The manual inspection is very slow, costly, and dangerous.• An average error of 2% for predicting meme volume and 17% for predicting meme lifespan.PROPOSED SYSTEM:-• Foursquare, a popular location check-in service, the importance of analyzing social media as a communicative rather than representational system.• A quantitative analysis to maximize the relevance of information in networks with multiple information providers.• Develop a computational framework that checks, for any given topic, how necessary and sufficient each user group is in reaching a wide audience.• Larger depending on the classroom size, clarity level of the lecture, participation level of students, etc.• To monitor tweets and to detect a target event. To detect a target event, we devise a classifier of tweets based on features such as the keywords in a tweet, the number of words, and context.• Developing intellectual skills in order to promote scholarly thinking relating different ideas together to form a bigger picture.REQUIREMENT SPECIFICATION:-System Requirements:-Hardware Requirements:- System : Pentium IV 2.4 GHz Hard Disk : 40 GB Ram : 4 GBSoftware Requirements:- Operating system : Windows XP Technology Used : Microsoft .NET Backend Used : SQL Server
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