Top PDF Grassmann Learning for Recognition and Classification

Grassmann Learning for Recognition and  Classification

Grassmann Learning for Recognition and Classification

113 | P a g e 8 Conclusions The benefits of Grassmann learning for processing high dimensional data and easing computation loads were explored. This dissertation began by discussing high dimensional representations and radial distance surfaces were proposed. Such surfaces were found to be scale invariant, localization invariant, and time invariant for multi-view action classification. This was justified through manifold learning with LPP. However, the results indicate that the approach is not robust in terms of promoting high between-class discrimination and requires an exhaustive dictionary of action representations across multiple views. The next contribution in this dissertation is the definition of motion history surfaces (MHS) and motion depth surfaces (MDS) based on spatio-temporal considerations. These high dimensional surfaces were evaluated with dimensionality reduction algorithms including PCA, LGE, Spectral Regression, Grassmann learning, and Sparse Representations.
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Handwritten Digit Recognition and Classification Using Machine Learning

Handwritten Digit Recognition and Classification Using Machine Learning

Table 4.7: The summary of handwritten digit RR based on four classifier models As can be seen from Table 4.7, the combined performances of the CNN and K-NN models are higher than SVM and RF in the field of handwritten digit recognition. Furthermore, the performances of two combinations have successfully answered the challenge of this study and improved the accuracy to over 99%, respectively Preprocessing + CNN and Preprocessing + PCA + K-NN. Notably, the combination of pre-processing and CNN reached the highest efficiency of 99.44% throughout the experiment. However, an automatic extraction method LeNet5 by CNN can detect features directly from the original image, PCA and HOG technologies were not explored based on the CNN model. In contrast, most application SVM and RF models had recognition rates below 95% in general. An interesting finding is that the accuracy achieved from using the HOG feature descriptor based on K-NN and RF was lower than the raw data. One of the reasons may be that the two classification algorithms are not sensitive to the alignment of the intensity gradient of the image, and that will be the future research direction.
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Real Time Object Recognition and Classification using Deep Learning

Real Time Object Recognition and Classification using Deep Learning

ABSTRACT Navigation in indoor environments is highly challenging for visually impaired person, particularly in spaces visited for the first time. Various solutions have been proposed to deal with this challenge. In this project consider as the real time object Recognition and classification using deep learning algorithms. Object detection mainly deals with identification of real time objects such as people, animals, and objects. Object detection algorithm uses a wide range of image processing applications for extracting the object’s desired portion. This enables one to identify the objects and calculate the accuracy of the object and deliver through voice. Using this information, the system determines the user’s trajectory and can locate possible obstacles in that route.
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Hair Color Classification in Face Recognition using Machine Learning Algorithms

Hair Color Classification in Face Recognition using Machine Learning Algorithms

detection are based on color [1] and region selection [3]. Unfortunately, not many papers were found on the Internet or at the Concordia University library about hair color and region selection, which could indicate the possibility of additional work in this field and the publication of more papers. In the color based approach (which will be explained more thoroughly in upcoming chapters), RGB and HSV values are considered in feature extraction, but in the region-based algorithm, a geometrical model for hair is proposed. Following the feature extraction step, it is possible to use a classifier in most cases. The most useful classifier that has been mentioned in the extant literature is the Super Vector Machine[1,2,5]. Some other classifiers, such as the Artificial Neural Network [6,7] and the K th Nearest Neighborhood [5], have been used as well. The focus of this experience in classification is on the Super Vector Machine, or the SVM. This tool is a statistical method that has been identified as one of the strongest tools in pattern recognition. As is well-known, this supervised learning method is especially used for classification, and the most important characteristic that makes it very famous is its strong reputation in the field of handwriting recognition. The main idea belongs to “Vapnik 1998,” which is represented as “apply a linear method to the data but in a high dimensional feature space” [8]. “Radial Basis Function called RBF” is a popular kernel function [9] described by:
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TimeML Events Recognition and Classification: Learning CRF Models with Semantic Roles

TimeML Events Recognition and Classification: Learning CRF Models with Semantic Roles

1 Introduction Event recognition and classification has been pointed out to be very important to improve com- plex natural language processing (NLP) applica- tions such as automatic summarization (Daniel et al., 2003) and question answering (QA) (Puste- jovsky, 2002). Natural language (NL) texts often describe sequences of events in a time line. In the context of summarization, extracting such events may aid in obtaining better summaries when these have to be focused on specific happenings. In the same manner, the access to such information is crucial for QA systems attempting to address questions about events.
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Learning discriminative tree edit similarities for linear classification — Application to melody recognition

Learning discriminative tree edit similarities for linear classification — Application to melody recognition

Many applications require to deal with hierarchical information repre- sented as trees such as Web/XML data processing in web information re- trieval, syntactic analysis in natural language processing, structured databases, structured representations in images or symbolic representations in music in- formation retrieval. In these areas, pattern recognition algorithms are used to perform classification or clustering tasks. Many of them rely on a notion of distance or similarity such as the k-Nearest-Neighbors, k-means or kernel- based classifiers. In the context of tree-structured data, tree edit distance (TED) (Bille, 2005) is a widely-used measure that extends the concept of string edit distance. TED is defined as the least costly set of basic oper- ations to transform one tree into another. Typically, these edit operations include substitution, insertion or deletion of tree nodes.
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SYMBOLIC AND NEURAL LEARNING OF NAMED-ENTITY RECOGNITION AND CLASSIFICATION SYSTEMS IN TWO LANGUAGES

SYMBOLIC AND NEURAL LEARNING OF NAMED-ENTITY RECOGNITION AND CLASSIFICATION SYSTEMS IN TWO LANGUAGES

e-mail: {petasis, petridis, paliourg, vangelis, sper, costass}@iit.demokritos.gr Abstract This paper compares two alternative approaches to the problem of acquir- ing named-entity recognition and classification systems from training cor- pora, in two different languages. The process of named-entity recognition and classification is an important subtask in most language engineering applications, in particular information extraction, where different types of named entity are associated with specific roles in events. The manual con- struction of rules for the recognition of named entities is a tedious and time-consuming task. For this reason, effective methods to acquire such systems automatically from data are very desirable. In this paper we com- pare two popular learning methods on this task: a decision-tree induction method and a multi-layered feed-forward neural network. Particular em- phasis is paid on the selection of the appropriate data representation for each method and the extraction of training examples from unstructured textual data. We compare the performance of the two methods on large corpora of English and Greek texts and present the results. In addition to the good performance of both methods, one very interesting result is the fact that a simple representation of the data, which ignores the order of the words within a named entity, leads to improved results over a more com- plex approach that preserves word order.
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Coin Recognition and Classification: A Review

Coin Recognition and Classification: A Review

Categorization is based on images from both sides as well as a radius of the coin. Keywords: Image Processing, Artificial Neural Networks, Edge detection, Features extraction I. INTRODUCTION Nowadays, antique coins [1] are becoming subject to a very large illegitimate trade. Thus, the interest in reliable automatic coin recognition systems in cultural heritage as well as law enforcement institutions rises rapidly. Usual methods to fight the illicit traffic of ancient coins comprise manual, periodical search in auctions catalogues, field search by authority forces and the periodical controls at expert dealers, also a unwieldy and unrewarding internet search, followed by human investigation. Applied pattern recognition algorithms are various ranging from neural networks to eigen spaces, decision trees, edge detection as well as gradient directions, and contour with texture features. Tests performed on image collections both of medieval with indian modern coins show that algorithms performing good quality on Indian modern coins do not necessarily meet the wants for classification of medieval ones. Major difference between ancient and Indian modern coins is that the indian ancient coins [1] have no rotating symmetry and subsequently their diameter is unknown. Since ancient coins are all too often in very unfortunate conditions, common recognition algorithms can effortlessly fail. The description that most influence the quality of recognition process are yet unexplored. The COINS project addresses this investigation gap and aims to give an efficient image based algorithms for coin categorization as well as identification. There is a basic need of highly perfect and efficient automatic coin recognition systems in our everyday life. Coin recognition systems as well as the coin sorting machines have become an essential part of our life. They are used in banks, vending machines, grocery stores, supermarkets etc. In-spite of daily uses coin recognition systems can also be used for the investigate purpose by the institutes or organizations that deal with the ancient coins. There are three types of coin recognition systems based on dissimilar methods used by them accessible in the market:
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Using Machine Learning to Maintain Rule based Named Entity Recognition and Classification Systems

Using Machine Learning to Maintain Rule based Named Entity Recognition and Classification Systems

Named-entity recognition and classification (NERC) is the identification of proper names in text and their classification as different types of named entity (NE), e.g. persons, organisations, locations, etc. This is an important subtask in most language engineering applications, in par- ticular information retrieval and extraction. The lexical resources that are typically included in a NERC system are a lexicon, in the form of gaz- etteer lists, and a grammar, responsible for rec- ognising the entities that are either not in the lexicon or appear in more than one gazetteer lists. The manual adaptation of those two re- sources to a particular domain is time- consuming and in some cases impossible, due to the lack of experts. The exploitation of learning techniques to support this adaptation task has attracted the attention of researchers in language engineering.
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LEARNING EMBEDDINGS FOR INDEXING, RETRIEVAL, AND CLASSIFICATION, WITH APPLICATIONS TO OBJECT AND SHAPE RECOGNITION IN IMAGE DATABASES

LEARNING EMBEDDINGS FOR INDEXING, RETRIEVAL, AND CLASSIFICATION, WITH APPLICATIONS TO OBJECT AND SHAPE RECOGNITION IN IMAGE DATABASES

Boston University, Graduate School of Arts and Sciences, 2006 Major Professor: Stan Sclaroff, Department of Computer Science ABSTRACT Nearest neighbor retrieval is the task of identifying, given a database of objects and a query object, the objects in the database that are the most similar to the query. Retrieving nearest neighbors is a necessary component of many practical applications, in fields as diverse as computer vision, pattern recognition, multimedia databases, bioinformatics, and computer networks. At the same time, finding nearest neighbors accurately and efficiently can be challenging, especially when the database contains a large number of objects, and when the underlying distance measure is computationally expensive. This thesis proposes new methods for improving the efficiency and accuracy of nearest neighbor retrieval and classification in spaces with computationally expensive distance measures. The proposed methods are domain-independent, and can be applied in arbitrary spaces, including non- Euclidean and non-metric spaces. In this thesis particular emphasis is given to computer vision applications related to object and shape recognition, where expensive non-Euclidean distance measures are often needed to achieve high accuracy.
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Recognition and Classification of Fast Food Images

Recognition and Classification of Fast Food Images

These test features are then passed to the classifier to calculate the accuracy of the trained classifier. V. E XPERIMENTAL R ESULT Our proposed system creates a classifier depending on the extracted features of CNN for identification of the object. The obtained success rate of recognition and classification has been represented using a confusion matrix. A confusion matrix also called an error matrix is a contingency table that comprise of the information about actual and predicted classifications done by a classification system. Fig 10 and Fig 11 shows the confusion matrix that appraises the Accuracy rate of the classification using our algorithm.
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Classification Techniques for Speech Recognition: A Review

Classification Techniques for Speech Recognition: A Review

Various fields for research in speech processing are speech recognition, speaker recognition, speech synthesis, speech coding etc. Speech recognition is the process of automatically recognizing the spoken words of person based on information content in speech signal. This paper introduces a brief survey on Automatic Speech Recognition and discusses the various classification techniques that have been accomplished in this wide area of speech processing. The objective of this review paper is to summarize some of the well-known methods that are widely used in several stages of speech recognition system.
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Leaf Recognition And Classification Techniques-Survey

Leaf Recognition And Classification Techniques-Survey

dhan_mak@yahoo.com Abstract Plants are an indispensable part of our ecosystem and globally it has a long history of using plants as a source of medicines. Since the advent of modern allopathic medicine, the use of traditional medicine declined to a considerable extent. However, in recent years, traditional medicine has made a comeback for a variety of reasons like they are inexpensive, nontoxic and does not impact any side effect. Different kind of medicinal plant species are available on earth but it is very difficult to identify the plant. Considerable knowledge accumulated by the villagers and tribal on medicine from plants remains unknown to the scientists and urban people. This paper explores survey a various laves recognition and classification of both ayurvedic and normal plant. The main objective of the survey is to know various classification Techniques and how effectively utilizing in Ayurvedic Plants recognition by various feature Extraction Methods.
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Ayurvedic leaf recognition for Plant Classification

Ayurvedic leaf recognition for Plant Classification

Poonjar, Kottayam District, Kerala, India Abstract-There are a lot of techniques relevant for the purpose automated leaf recognition for plant classification. Many algorithms have been introduced in the past decade and achieved good performance. Efforts have focused upon many other fields but properties of features have not been well investigated. A group of features is selected in advance but important feature properties are not well used to feature selection. In this paper the performance of different features extraction methods are compared, different combinations of features and a number of classifiers applied for leaf identification process are also discussed.
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Recognition Using Classification and Segmentation Scoring

Recognition Using Classification and Segmentation Scoring

Recognition Using Classification and Segmentation Scoring Recognition Using Classification and Segmentation Scoring* Owen Kimball t, Mari Ostendorf t, Robin Rohlicek t Boston University ~ B B N Inc 44[.]

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Classification complexity in myoelectric pattern recognition

Classification complexity in myoelectric pattern recognition

namely minimum redundancy and maximum rele- vance [10], and Markov random fields [11], were ap- plied to an electrode array by Liu et al. [12], who used Kullback–Leibler divergence and feature scatter to rate the relevance and redundancy of features. The features were then ranked and selected into sets ac- cording to these ratings. Similarly, Bunderson et al. defined three data quality indices – namely, repeat- ability index (RI), mean semi-principal axis, and sep- arability index (SI) – to evaluate the changes in data quality over repeated recordings of EMG [13]. Classification complexity estimation was not investi- gated in the aforementioned studies, but algorithms intended to quantify attributes relevant to the com- plexity of pattern recognition tasks were introduced.
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Computer Vision (Recognition, Detection and Classification Problems) Deep Learning Machine Learning/Pattern Recognition Data Science

Computer Vision (Recognition, Detection and Classification Problems) Deep Learning Machine Learning/Pattern Recognition Data Science

2004 9 th Team Rank , 6th Asia Regional ACM Programming Contest along with the "UT1" team members, 72 teams participated from Iran, Sharif site, Tehran.. 2004, 2005, 2006.[r]

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Machine Learning Algorithms for Image Classification of Hand Digits and Face Recognition Dataset

Machine Learning Algorithms for Image Classification of Hand Digits and Face Recognition Dataset

3. DATABASE DESCRIPTION In this study, two dataset were obtained (e.g. MNIST, ORL). A. MNIST The source of MNIST dataset can be found in [16]. This database of handwritten digits, available from this page, has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size image. It is a good database for people who want to try learning techniques and pattern recognition methods on real-world data while spending minimal efforts on preprocessing and formatting.
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Learning Discriminative Tree Edit Similarities for Linear Classification - Application to Melody Recognition

Learning Discriminative Tree Edit Similarities for Linear Classification - Application to Melody Recognition

Finally, we provide a brief analysis of the reasonable points automatically selected by solving problem (2) of Section 3.3. Intuitively, these representa- tive points should be some discriminative prototypes the classifier is based on. To make the analysis easier, we consider a restricted task where the goal is to predict if a melody belongs to the classical music genre or if it is a children’s song. These two styles correspond to two classes allowing us to turn the task into a binary classification problem. The examples of the classical genre cor- respond to songs belonging to the classes ‘Toccata and fugue’, ‘Avemaria’, ‘Ode to joy’, ‘Bolero’ and ‘Lohengrin, wedding march’ of the Pascal cor- pus. The children’s class is formed by the songs coming from ‘Alouette’, ‘Oh! Susanna’, ‘Happy birthday’, ‘Twinkle twinkle little star’ and ‘Jingle bells’ classes. From these data, we build a learning and a test sample such that there are 7 instances for each Pascal class in the test and 14 in the training set. Therefore, each class has 35 examples in test and 70 for train- ing. Table 2 shows the number of reasonable points for each class. We can see that (beyond a high accuracy) in the classical class there are 4 reasonable points that belong to ‘Toccata and fugue’ and 5 to ‘Lohengrin, wedding march’. These two songs seem thus to be good representatives for that class. In the same way, the ‘Oh! Susanna’ and ‘Happy birthday’ songs provide many (9 out of 15) discriminative prototypes for the children class. All in all, these four songs provide about 62% (18 out of 29) of the reasonable points while they represent only 40% of the training songs. Said differently, the
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Spam Recognition Based on Bayesian Classification

Spam Recognition Based on Bayesian Classification

Spam Recognizer Based on Bayesian Classification Algorithm The structure of spam recognizer based on improved Bayesian is shown as Fig.2. First, create spam and legal corpus. Then, according to Bayesian training algorithm, divide each email into tokens, statistic the occurrence of each token. After that create hash-spam and hash-legal table. Meanwhile, calculate spam probability of each token contribute to, create hash-probability table. Use these three table create Bayesian knowledge base. When new email arrives, detect URL, images and text, make the judgment with the help of Bayesian knowledge base and classification algorithm. At last, Feedback classified email into corpuses to prepare for new training process. After several feedback, start Bayesian training again if corpuses reach a certain fluctuation range, update hash-spam, hash-legal and hash-probability table to maintain timeliness of Bayesian knowledge base.
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