Computer-Aided Plant Species Identification acts significantly on plant digital museum system and systematic botany, which is the groundwork for research and development of plant. this paper presents a new method for ...
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ISBN:
(纸本)9780769533049
Computer-Aided Plant Species Identification acts significantly on plant digital museum system and systematic botany, which is the groundwork for research and development of plant. this paper presents a new method for plant species identification using leaf image. It focuses on the stable features extraction of leaf such as the geometrical features of shape and the texture features of venation. the 2-D moment invariants, Wavelet statistical features are used to extract leaf information. Self-Organizing Feature Map (SOM) neural network has the advantages of simple structure, ordered mapping topology and low complexity of learning. It is suitable for many complex problems such as multi-class patternrecognition, high dimension input vector and large quantity training data. So this paper use SOM neural network to identify the plant species. the experimental results illustrate the effectiveness of this method.
Withthe growing use of computers and the Internet, it has become difficult for organizations to locate and effectively manage sensitive personally identifiable information (PII). this problem becomes even more eviden...
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A modified version for semi-supervised learning algorithm with local and global consistency was proposed in this paper. the new method adds the label information, and adopts the geodesic distance rather than Euclidean...
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ISBN:
(纸本)9781424421749
A modified version for semi-supervised learning algorithm with local and global consistency was proposed in this paper. the new method adds the label information, and adopts the geodesic distance rather than Euclidean distance as the measure of the difference between two data points when conducting calculation. In addition we add class prior knowledge. It was found that the effect of class prior knowledge was different between under high label rate and low label rate. the experimental results show that the changes attain the satisfying classification performance better than the original algorithms.
Associative classifiers that utilize association rules have been widely studied. It has been shown that associative classifiers often outperform traditional classifiers. Associative classifiers usually find only rules...
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Associative classifiers that utilize association rules have been widely studied. It has been shown that associative classifiers often outperform traditional classifiers. Associative classifiers usually find only rules with high support values, because reducing the minimum support to be satisfied increases computational cost. However, rules with low support but high confidence may contribute to classification. We have proposed an approach to build a classifier composed of almost all consistent (100% confident) rules. the proposed classifier was extended by introducing item reduction and bagging in order to relax the constraint of consistency, which resulted in slightly increased performance for 26 datasets from the UCI machinelearning repository.
In real-world digital libraries, artificial intelligence techniques are essential for tackling the automatic document processing task with sufficient flexibility. the great variability in document kind, content and sh...
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ISBN:
(纸本)9781424421749
In real-world digital libraries, artificial intelligence techniques are essential for tackling the automatic document processing task with sufficient flexibility. the great variability in document kind, content and shape requires powerful representation formalisms to catch all the domain complexity. the continuous flow of new documents requires adaptable techniques that can progressively adjust the acquired knowledge on documents as long as new evidence becomes available, even extending if needed the set of recognized document types. Boththese issues have not yet been thoroughly studied. this paper presents an incremental first-order logic learning framework for automatically dealing with various kinds of evolution in digital repositories content: evolution in the definition of class definitions, evolution in the set of known classes and evolution by addition of new unknown classes. Experiments show that the approach can be applied to real-world.
Summarizing and understanding video shot based on their contents is an important research topic in multimedia datamining. this paper presents an efficient algorithm based on optical flow field to mine the motion data...
Summarizing and understanding video shot based on their contents is an important research topic in multimedia datamining. this paper presents an efficient algorithm based on optical flow field to mine the motion data contained in a given video shot. Two features called magnitude of motion pixel and direction of motion pixel are constructed respectively and adopted to split the video shot into some categories automatically. the corresponding indexical structure is extracted from each category and directly applied to motion-based queries. A test system has been developed to prove the validity of our algorithm. the experimental results show that the algorithm performs well and will play an important role in content-based video shot retrieval.
In this paper we propose a confidence rated boosting algorithm based on Ada-boost for generic object detection. Confidence rated Ada-boost algorithm has not been applied to generic object detection problem; in that se...
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ISBN:
(纸本)9781424421749
In this paper we propose a confidence rated boosting algorithm based on Ada-boost for generic object detection. Confidence rated Ada-boost algorithm has not been applied to generic object detection problem; in that sense our work is novel. We represent images as bag of words, where the words are SIFT descriptors extracted over some interest points. We compare our boosting algorithm to another version of boosting algorithm called Gentle-boost. Our approach generalizes well and performs equal or better than Gentle-boost. We show our results on four categories from the Caltech data sets, in terms of ROC curves.
Face recognition is nowadays one of the most challenging biometric modalities for the identification of individuals. In the last two decades several experimental as well as commercial systems have been developed explo...
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Face recognition is nowadays one of the most challenging biometric modalities for the identification of individuals. In the last two decades several experimental as well as commercial systems have been developed exploiting different physical properties of the face image. Either being based on processing 2D or 3D information all these methods perform a face classification of the individuals based on some relevant features extracted from the raw image data. the data acquisition, preprocessing and the feature extraction/selection are all topics of the greatest importance to design a good performing recognition system. At the same time, the right choice of the features to be used as the basis for the face representation, which must be based on the uniqueness of such features, as well as most advanced issues such as the incorporation of quality information and the cope for ageing effects, are all of paramount importance. the tutorial will consists of two sessions (half day of total duration) devoted to the description of boththe basic and most advanced techniques related to face recognition. the lectures will provide a comprehensive outline of face- based biometrics, its relation to biological systems (the psychophysics of the human visual system), including the existing applications and commercial systems. the lectures will provide an in-depth analysis of the state-of-the-art algorithms for face-image analysis including: face detection and tracking, landmark localization, feature extraction, face representation and classification. the lectures will mainly explore the image processing aspects of the recognition process. As for classification, machinelearning algorithms will be also presented, including kernel methods as related to learning and the approximation theory. the most relevant issues and problems will be raised, providing practical solutions and algorithms responding to them. Particular attention will be given to the most advanced and new techniques for face
In this paper, we propose sparse non-negative patternlearning (SNPL) based on self-taught learning framework. In the algorithm, visual patterns are first learned from unlabeled data by non-negative matrix approximati...
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In this paper, we propose sparse non-negative patternlearning (SNPL) based on self-taught learning framework. In the algorithm, visual patterns are first learned from unlabeled data by non-negative matrix approximation with sparseness constraints, and then features are extracted by the second part of the algorithm, a conjugate family based non-negative sparse feature extraction method. By combining sparse and non-negative constraints of patterns together, SNPL model gives a better representation for images than state-of-art methods. Beyond that, we give an analytical solution for feature extraction although it is approximate, and thereby we extract the features for self-taught learning framework in a faster and more stable way. We apply the new model to various areas, including pattern coding, feature extraction, and recognition. Experimental results show the advantages of SNPL model.
In this paper, we investigate if rule-based systems are useful for image classification problems when the number of classes is fixed. the rules are derived from simple edge features such as width and straightness. A c...
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ISBN:
(纸本)9781424421749
In this paper, we investigate if rule-based systems are useful for image classification problems when the number of classes is fixed. the rules are derived from simple edge features such as width and straightness. A class representative is calculated for each class according to the average percentage of edges that satisfy the rule for a particular class. this percentage for an unknown image is compared to the class representative to assign a label to it. the proposed system does not require extensive feature extraction and classification techniques. It is shown that the rule based system outperforms some of the reported results on scene classification.
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