Granular Flow Graph, Adaptive Rule Generation and Tracking
01 Dec 2017-IEEE Transactions on Systems, Man, and Cybernetics (IEEE Trans Cybern)-Vol. 47, Iss: 12, pp 4096-4107
TL;DR: A new method of adaptive rule generation in granular computing framework is described based on rough rule base and granular flow graph, and applied for video tracking, and it is shown that the neighborhood granulation provides a balanced tradeoff between speed and accuracy as compared to pixel level computation.
Abstract: A new method of adaptive rule generation in granular computing framework is described based on rough rule base and granular flow graph, and applied for video tracking. In the process, several new concepts and operations are introduced, and methodologies formulated with superior performance. The flow graph enables in defining an intelligent technique for rule base adaptation where its characteristics in mapping the relevance of attributes and rules in decision-making system are exploited. Two new features, namely, expected flow graph and mutual dependency between flow graphs are defined to make the flow graph applicable in the tasks of both training and validation. All these techniques are performed in neighborhood granular level. A way of forming spatio-temporal 3-D granules of arbitrary shape and size is introduced. The rough flow graph-based adaptive granular rule-based system, thus produced for unsupervised video tracking, is capable of handling the uncertainties and incompleteness in frames, able to overcome the incompleteness in information that arises without initial manual interactions and in providing superior performance and gaining in computation time. The cases of partial overlapping and detecting the unpredictable changes are handled efficiently. It is shown that the neighborhood granulation provides a balanced tradeoff between speed and accuracy as compared to pixel level computation. The quantitative indices used for evaluating the performance of tracking do not require any information on ground truth as in the other methods. Superiority of the algorithm to nonadaptive and other recent ones is demonstrated extensively.
Citations
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TL;DR: This study identifies the principles of Granular Computing, shows how information granules are constructed and subsequently used in describing relationships present among the data, and advocates that the level of abstraction can be flexibly adjusted through Granular computing.
Abstract: In the plethora of conceptual and algorithmic developments supporting data analytics and system modeling, humancentric pursuits assume a particular position owing to ways they emphasize and realize interaction between users and the data. We advocate that the level of abstraction, which can be flexibly adjusted, is conveniently realized through Granular Computing. Granular Computing is concerned with the development and processing information granules – formal entities which facilitate a way of organizing knowledge about the available data and relationships existing there. This study identifies the principles of Granular Computing, shows how information granules are constructed and subsequently used in describing relationships present among the data.
108 citations
TL;DR: In this article, the authors provide a comprehensive overview of object detection and tracking using deep learning (DL) networks and compare the performance of different object detectors and trackers, including the recent development in granulated DL models.
Abstract: Object detection and tracking is one of the most important and challenging branches in computer vision, and have been widely applied in various fields, such as health-care monitoring, autonomous driving, anomaly detection, and so on. With the rapid development of deep learning (DL) networks and GPU’s computing power, the performance of object detectors and trackers has been greatly improved. To understand the main development status of object detection and tracking pipeline thoroughly, in this survey, we have critically analyzed the existing DL network-based methods of object detection and tracking and described various benchmark datasets. This includes the recent development in granulated DL models. Primarily, we have provided a comprehensive overview of a variety of both generic object detection and specific object detection models. We have enlisted various comparative results for obtaining the best detector, tracker, and their combination. Moreover, we have listed the traditional and new applications of object detection and tracking showing its developmental trends. Finally, challenging issues, including the relevance of granular computing, in the said domain are elaborated as a future scope of research, together with some concerns. An extensive bibliography is also provided.
104 citations
TL;DR: An attribute-driven granular model (AGrM) under a machine-learning scheme that achieved comparable pinch recognition accuracy but was of lowest computational cost and highest force grand prediction accuracy.
Abstract: Fine multifunctional prosthetic hand manipulation requires precise control on the pinch-type and the corresponding force, and it is a challenge to decode both aspects from myoelectric signals. This paper proposes an attribute-driven granular model (AGrM) under a machine-learning scheme to solve this problem. The model utilizes the additionally captured attribute as the latent variable for a supervised granulation procedure. It was fulfilled for EMG-based pinch-type classification and the fingertip force grand prediction. In the experiments, 16 channels of surface electromyographic signals (i.e., main attribute) and continuous fingertip force (i.e., subattribute) were simultaneously collected while subjects performing eight types of hand pinches. The use of AGrM improved the pinch-type recognition accuracy to around 97.2% by 1.8% when constructing eight granules for each grasping type and received more than 90% force grand prediction accuracy at any granular level greater than six. Further, sensitivity analysis verified its robustness with respect to different channel combination and interferences. In comparison with other clustering-based granulation methods, AGrM achieved comparable pinch recognition accuracy but was of lowest computational cost and highest force grand prediction accuracy.
31 citations
Additional excerpts
...groups, classes or clusters of a universe, which is closely related to the cognitive strategy of human being in problem solving and it is technically transferable to the design of human-centric intelligent systems [17], which has been applied in image-based crowd segmentation [18], longterm prediction model for the energy system [19], [20], video based object tracking [21] and principle curve extraction [22], etc....
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TL;DR: Theoretical analysis and experimental results show that FGKNN and BFGKNN have better performance than that of the methods mentioned above if the appropriate parameters are given.
Abstract: K-nearest neighbor (KNN) is a classic classifier, which is simple and effective. Adaboost is a combination of several weak classifiers as a strong classifier to improve the classification effect. These two classifiers have been widely used in the field of machine learning. In this paper, based on information fuzzy granulation, KNN and Adaboost, we propose two algorithms, a fuzzy granule K-nearest neighbor (FGKNN) and a boosted fuzzy granule K-nearest neighbor (BFGKNN), for classification. By introducing granular computing, we normalize the process of solving problem as a structured and hierarchical process. Structured information processing is focused, so the performance including accuracy and robust can be enhanced to data classification. First, a fuzzy set is introduced, and an atom attribute fuzzy granulation is performed on samples in the classified system to form fuzzy granules. Then, a fuzzy granule vector is created by multiple attribute fuzzy granules. We design the operators and define the measure of fuzzy granule vectors in the fuzzy granule space. And we also prove the monotonic principle of the distance of fuzzy granule vectors. Furthermore, we also give the definition of the concept of K-nearest neighbor fuzzy granule vector and present FGKNN algorithm and BFGKNN algorithm. Finally, we compare the performance among KNN, Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), Logistic Regression (LR), FGKNN and BFGKNN on UCI data sets. Theoretical analysis and experimental results show that FGKNN and BFGKNN have better performance than that of the methods mentioned above if the appropriate parameters are given.
31 citations
TL;DR: The article deals with the problems of motion detection, object recognition, and scene description using deep learning in the framework of granular computing and Z-numbers and shows a balanced trade-off between speed and accuracy as compared to pixel level learning in tracking and recognition.
Abstract: The article deals with the problems of motion detection, object recognition, and scene description using deep learning in the framework of granular computing and Z-numbers. Since deep learning is computationally intensive, whereas granular computing, on the other hand, leads to computation gain, a judicious integration of their merits is made so as to make the learning mechanism computationally efficient. Further, it is shown how the concept of z-numbers can be used to quantify the abstraction of semantic information in interpreting a scene, where subjectivity is of major concern, through recognition of its constituting objects. The system, thus developed, involves recognition of both static objects in the background and moving objects in foreground separately. Rough set theoretic granular computing is adopted where rough lower and upper approximations are used in defining object and background models. During deep learning, instead of scanning the entire image pixel by pixel in the convolution layer, we scan only the representative pixel of each granule. This results in a significant gain in computation time. Arbitrary-shaped and sized granules, as expected, perform better than regular-shaped rectangular granules or fixed-sized granules. The method of tracking is able to deal efficiently with various challenging cases, e.g., tracking partially overlapped objects and suddenly appeared objects. Overall, the granulated system shows a balanced trade-off between speed and accuracy as compared to pixel level learning in tracking and recognition. The concept of using Z-numbers, in providing a granulated linguistic description of a scene, is unique. This gives a more natural interpretation of object recognition in terms of certainty toward scene understanding.
19 citations
References
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TL;DR: Experimental results on real-world data sets demonstrate the superiority of ROSE, both in terms of some quantitative indices and outliers detected, over those obtained by various rough fuzzy clustering algorithms and by the state-of-the-art outlier detection methods.
Abstract: Nowadays, the high availability of data gathered from wireless sensor networks and telecommunication systems has drawn the attention of researchers on the problem of extracting knowledge from spatiotemporal data. Detecting outliers which are grossly different from or inconsistent with the remaining spatiotemporal data set is a major challenge in real-world knowledge discovery and data mining applications. In this paper, we deal with the outlier detection problem in spatiotemporal data and describe a rough set approach that finds the top outliers in an unlabeled spatiotemporal data set. The proposed method, called Rough Outlier Set Extraction (ROSE), relies on a rough set theoretic representation of the outlier set using the rough set approximations, i.e., lower and upper approximations. We have also introduced a new set, named Kernel Set, that is a subset of the original data set, which is able to describe the original data set both in terms of data structure and of obtained results. Experimental results on real-world data sets demonstrate the superiority of ROSE, both in terms of some quantitative indices and outliers detected, over those obtained by various rough fuzzy clustering algorithms and by the state-of-the-art outlier detection methods. It is also demonstrated that the kernel set is able to detect the same outliers set but with less computational time.
59 citations
Additional excerpts
...ery [3], [7]....
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TL;DR: It is revealed that flow in a flow graph is governed by Bayes’ rule, but the rule has an entirely deterministic interpretation without referring to its probabilistic roots.
Abstract: In this paper we propose a new approach to data mining and knowledge discovery based on information flow distribution in a flow graph. Flow graphs introduced in this paper are different from those proposed by Ford and Fulkerson for optimal flow analysis and they model flow distribution in a network rather than the optimal flow which is used for information flow examination in decision algorithms. It is revealed that flow in a flow graph is governed by Bayes’ rule, but the rule has an entirely deterministic interpretation without referring to its probabilistic roots. Besides, a decision algorithm induced by a flow graph and dependency between conditions and decisions of decision rules is introduced and studied, which is used next to simplify decision algorithms.
58 citations
"Granular Flow Graph, Adaptive Rule ..." refers background or methods in this paper
...Information flow graph was introduced [25] to model the...
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...I. INTRODUCTION THEORY of rough sets, as explained by Pawlak [1] dealtwith uncertainties or incompleteness of knowledge arising from the limited discernibility of objects in the domain of discourse....
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...A brief discussion on neighborhood rough sets (NRSs) [34], [35] which is a new variant of Pawlak’s rough set [1], and its relevance to video tracking were described in our earlier work [33]....
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...Flow graph was introduced by Pawlak [25]...
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...One may note that certainty and covariance are only the two features explored by Pawlak [25] while defining flow graph....
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TL;DR: A novel hierarchical background model for intelligent video surveillance with the pan-tilt-zoom (PTZ) camera is studied, and an integrated system consisting of three key components: background modeling, observed frame registration, and object tracking is given rise to.
Abstract: In this paper, we study a novel hierarchical background model for intelligent video surveillance with the pan-tilt-zoom (PTZ) camera, and give rise to an integrated system consisting of three key components: background modeling, observed frame registration, and object tracking. First, we build the hierarchical background model by separating the full range of continuous focal lengths of a PTZ camera into several discrete levels and then partitioning the wide scene at each level into many partial fixed scenes. In this way, the wide scenes captured by a PTZ camera through rotation and zoom are represented by a hierarchical collection of partial fixed scenes. A new robust feature is presented for background modeling of each partial scene. Second, we locate the partial scenes corresponding to the observed frame in the hierarchical background model. Frame registration is then achieved by feature descriptor matching via fast approximate nearest neighbor search. Afterwards, foreground objects can be detected using background subtraction. Last, we configure the hierarchical background model into a framework to facilitate existing object tracking algorithms under the PTZ camera. Foreground extraction is used to assist tracking an object of interest. The tracking outputs are fed back to the PTZ controller for adjusting the camera properly so as to maintain the tracked object in the image plane. We apply our system on several challenging scenarios and achieve promising results.
57 citations
"Granular Flow Graph, Adaptive Rule ..." refers background in this paper
...Some such examples are, multiple cameras [27], PTZ camera [28], and Kinect sensor [29], [30]....
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TL;DR: A novel histogram thresholding methodology using fuzzy and rough set theories to evaluate image segmentation performance using the median of absolute deviation from median measure, which is a robust estimator of scale.
Abstract: This paper presents a novel histogram thresholding methodology using fuzzy and rough set theories. The strength of the proposed methodology lies in the fact that it does not make any prior assumptions about the histogram unlike many existing techniques. For bilevel thresholding, every element of the histogram is associated with one of the two regions by comparing the corresponding errors of association. The regions are considered ambiguous in nature, and, hence, the error measures are based on the fuzziness or roughness of the regions. Multilevel thresholding is carried out using the proposed bilevel thresholding method in a tree structured algorithm. Segmentation, object/background separation, and edge extraction are performed using the proposed methodology. A quantitative index to evaluate image segmentation performance is also proposed using the median of absolute deviation from median measure, which is a robust estimator of scale. Extensive experimental results are given to demonstrate the effectiveness of the proposed methods in terms of both qualitative and quantitative measures.
49 citations
"Granular Flow Graph, Adaptive Rule ..." refers background in this paper
..., feature reduction and selection [2], [3], image processing [4]–[6], data mining, and knowledge discov-...
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...2600271 to deal with the uncertainties arising from grayness and spatial ambiguities the concept of set approximation [4]–[6], [8]....
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TL;DR: The proposed appearance model outperforms the other state-of-the-art approaches in tracking performance and integrates with the particle filter framework to form a robust visual tracking algorithm.
Abstract: In this paper, we present a novel appearance model using sparse representation and online dictionary learning techniques for visual tracking. In our approach, the visual appearance is represented by sparse representation, and the online dictionary learning strategy is used to adapt the appearance variations during tracking. We unify the sparse representation and online dictionary learning by defining a sparsity consistency constraint that facilitates the generative and discriminative capabilities of the appearance model. An elastic-net constraint is enforced during the dictionary learning stage to capture the characteristics of the local appearances that are insensitive to partial occlusions. Hence, the target appearance is effectively recovered from the corruptions using the sparse coefficients with respect to the learned sparse bases containing local appearances. In the proposed method, the dictionary is undercomplete and can thus be efficiently implemented for tracking. Moreover, we employ a median absolute deviation based robust similarity metric to eliminate the outliers and evaluate the likelihood between the observations and the model. Finally, we integrate the proposed appearance model with the particle filter framework to form a robust visual tracking algorithm. Experiments on benchmark video sequences show that the proposed appearance model outperforms the other state-of-the-art approaches in tracking performance.
45 citations
"Granular Flow Graph, Adaptive Rule ..." refers background or methods in this paper
...It can be noticed from the table that the computation time is the lowest for the proposed method NRBFG with accuracy better than PLS, SPG, and GMOT, but slightly worse than CEMT and LLAS....
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...10, the red tracker shows the results obtained by NRBFG, the green tracker shows the results of PLS, the blue tracker shows the results of SPG, the yellow tracker shows the results obtained by CEMT, the magenta tracker shows the results obtained by LLAS, and the black tracker shows the results of GMOT....
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...10.1(a)], the performances for PLS, NRBFG, LLAS, and CEMT are almost equally good....
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...A few unsupervised approaches for tracking are recently proposed with statistical modeling of object motion [20], [23], [24]....
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...The tracking results for SPG are satisfactory for all of the four sequences, but the tracker fails to cover the entire moving objects, whereas both CEMT and LLAS give very satisfactory results even with multiple moving elements....
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