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  • 1
    Language: English
    In: Neural Computing and Applications, 2018, Vol.29(4), pp.943-957
    Description: Most existing content-based image retrieval and classification systems rely on low-level features which are automatically extracted from images. However, often these features lack the discrimination power needed for accurate description of the image content, and hence, they may lead to a poor retrieval or classification performance. We propose a novel technique to improve low-level features which uses parallel one-against-all perceptrons to synthesize new features with a higher discrimination power which in turn leads to improved classification and retrieval results. The proposed method can be applied on any database and low-level features as long as some ground-truth information is available. The main merits of the proposed technique are its simplicity and faster computation compared to existing feature synthesis methods. Extensive simulation results show a significant improvement in the features’ discrimination power.
    Keywords: Content-based image retrieval and classification ; Feature synthesis ; Multi-dimensional particle swarm optimization ; Multi-layer perceptrons
    ISSN: 0941-0643
    E-ISSN: 1433-3058
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  • 2
    Language: English
    In: Swarm and Evolutionary Computation, June 2017, Vol.34, pp.103-118
    Description: In this paper, we propose a new way to carry out fitness evaluation in dynamic Particle Swarm Clustering (PSC) with centroid-based encoding. Generally, the PSC fitness function is selected among the clustering validity indices and most of them directly depend on the cluster centroids. In the traditional fitness evaluation approach, the cluster centroids are replaced by the centroids proposed by a particle position. We propose to first compute the centroids of the corresponding clusters and then use these computational centroids in fitness evaluation. The proposed way is called Fitness Evaluation with Computational Centroids (FECC). We conducted an extensive set of comparative evaluations and the results show that FECC leads to a clear improvement in clustering results compared to the traditional fitness evaluation approach with most of the fitness functions considered in this study. The proposed approach was found especially beneficial when underclustering is a problem. Furthermore, we evaluated 31 fitness functions based on 17 clustering validity indices using two PSC methods over a large number of synthetic and real data sets with varying properties. We used three different performance criteria to evaluate the clustering quality and found out that the top three fitness functions are Xu index, WB index, and Dunn variant applied using FECC. These fitness functions consistently performed well for both PSC methods, for all data distributions, and according to all performance criteria. In all test cases, they were clearly among the better half of the fitness functions and, in the majority of the cases, they were among the top 4 functions. Further guidance for improved fitness function selection in different situations is provided in the paper.
    Keywords: Particle Swarm Optimization ; Pattern Clustering ; Validity Index ; Swarm Intelligence ; Computer Science
    ISSN: 2210-6502
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  • 3
    Language: English
    In: Journal of Big Data, 2016, Vol.3(1), pp.1-22
    Description: Graph clustering is an important technique to understand the relationships between the vertices in a big graph. In this paper, we propose a novel random-walk-based graph clustering method. The proposed method restricts the reach of the walking agent using an inflation function and a normalization function. We analyze the behavior of the limited random walk procedure and propose a novel algorithm for both global and local graph clustering problems. Previous random-walk-based algorithms depend on the chosen fitness function to find the clusters around a seed vertex. The proposed algorithm tackles the problem in an entirely different manner. We use the limited random walk procedure to find attractor vertices in a graph and use them as features to cluster the vertices. According to the experimental results on the simulated graph data and the real-world big graph data, the proposed method is superior to the state-of-the-art methods in solving graph clustering problems. Since the proposed method uses the embarrassingly parallel paradigm, it can be efficiently implemented and embedded in any parallel computing environment such as a MapReduce framework. Given enough computing resources, we are capable of clustering graphs with millions of vertices and hundreds millions of edges in a reasonable time.
    Keywords: Graph clustering ; Random walk ; Big data ; Community finding
    E-ISSN: 2196-1115
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  • 4
    Language: English
    In: EURASIP Journal on Audio, Speech, and Music Processing, 2012, Vol.2012(1), pp.1-23
    Description: A vast amount of audio features have been proposed in the literature to characterize the content of audio signals. In order to overcome specific problems related to the existing features (such as lack of discriminative power), as well as to reduce the need for manual feature selection, in this article, we propose an evolutionary feature synthesis technique with a built-in feature selection scheme. The proposed synthesis process searches for optimal linear/nonlinear operators and feature weights from a pre-defined multi-dimensional search space to generate a highly discriminative set of new (artificial) features. The evolutionary search process is based on a stochastic optimization approach in which a multi-dimensional particle swarm optimization algorithm, along with fractional global best formation and heterogeneous particle behavior techniques, is applied. Unlike many existing feature generation approaches, the dimensionality of the synthesized feature vector is also searched and optimized within a set range in order to better meet the varying requirements set by many practical applications and classifiers. The new features generated by the proposed synthesis approach are compared with typical low-level audio features in several classification and retrieval tasks. The results demonstrate a clear improvement of up to 15–20% in average retrieval performance. Moreover, the proposed synthesis technique surpasses the synthesis performance of evolutionary artificial neural networks , exhibiting a considerable capability to accurately distinguish among different audio classes.
    Keywords: Content-based retrieval ; Evolutionary computation ; Particle swarm optimization ; Feature selection ; Feature generation
    E-ISSN: 1687-4722
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  • 5
    Language: English
    In: IEEE Transactions on Neural Networks and Learning Systems, December 2016, Vol.27(12), pp.2458-2471
    Description: In training radial basis function neural networks (RBFNNs), the locations of Gaussian neurons are commonly determined by clustering. Training inputs can be clustered on a fully unsupervised manner (input clustering), or some supervision can be introduced, for example, by concatenating the input vectors with weighted output vectors (input-output clustering). In this paper, we propose to apply clustering separately for each class (class-specific clustering). The idea has been used in some previous works, but without evaluating the benefits of the approach. We compare the class-specific, input, and input-output clustering approaches in terms of classification performance and computational efficiency when training RBFNNs. To accomplish this objective, we apply three different clustering algorithms and conduct experiments on 25 benchmark data sets. We show that the class-specific approach significantly reduces the overall complexity of the clustering, and our experimental results demonstrate that it can also lead to a significant gain in the classification performance, especially for the networks with a relatively few Gaussian neurons. Among other applied clustering algorithms, we combine, for the first time, a dynamic evolutionary optimization method, multidimensional particle swarm optimization, and the class-specific clustering to optimize the number of cluster centroids and their locations.
    Keywords: Training ; Neurons ; Clustering Algorithms ; Optimization ; Neural Networks ; Heuristic Algorithms ; Particle Swarm Optimization ; Clustering Methods ; Particle Swarm Optimization (Pso) ; Radial Basis Function Networks (Rbfnns) ; Supervised Learning ; Computer Science
    ISSN: 2162-237X
    E-ISSN: 2162-2388
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  • 6
    Description: This paper proposes a novel method for solving one-class classification problems. The proposed approach, namely Subspace Support Vector Data Description, maps the data to a subspace that is optimized for one-class classification. In that feature space, the optimal hypersphere enclosing the target class is then determined. The method iteratively optimizes the data mapping along with data description in order to define a compact class representation in a low-dimensional feature space. We provide both linear and non-linear mappings for the proposed method. Experiments on 14 publicly available datasets indicate that the proposed Subspace Support Vector Data Description provides better performance compared to baselines and other recently proposed one-class classification methods. Comment: 6 pages, submitted/accepted, ICPR 2018
    Keywords: Computer Science - Computer Vision And Pattern Recognition
    Source: Cornell University
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  • 7
    Description: Graph clustering is an important technique to understand the relationships between the vertices in a big graph. In this paper, we propose a novel random-walk-based graph clustering method. The proposed method restricts the reach of the walking agent using an inflation function and a normalization function. We analyze the behavior of the limited random walk procedure and propose a novel algorithm for both global and local graph clustering problems. Previous random-walk-based algorithms depend on the chosen fitness function to find the clusters around a seed vertex. The proposed algorithm tackles the problem in an entirely different manner. We use the limited random walk procedure to find attracting vertices in a graph and use them as features to cluster the vertices. According to the experimental results on the simulated graph data and the real-world big graph data, the proposed method is superior to the state-of-the-art methods in solving graph clustering problems. Since the proposed method uses the embarrassingly parallel paradigm, it can be efficiently implemented and embedded in any parallel computing environment such as a MapReduce framework. Given enough computing resources, we are capable of clustering graphs with millions of vertices and hundreds millions of edges in a reasonable time. Comment: 12 pages, 3 figures, 7 tables, journal paper
    Keywords: Computer Science - Social And Information Networks ; Physics - Physics And Society
    Source: Cornell University
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  • 8
    Language: English
    In: 2018 7th European Workshop on Visual Information Processing (EUVIP), November 2018, pp.1-6
    Description: Deep learning algorithms (in particular Convolutional Neural Networks, or CNNs) have shown their superiority in computer vision tasks and continue to push the state of the art in the most difficult problems of the field. However, deep models frequently lack interpretability. Current research efforts are often focused on increasingly complex and computationally expensive structures. These can be either handcrafted or generated by an algorithm, but in either case the specific choices of individual structural elements are hard to justify. This paper aims to analyze statistical properties of a large sample of small deep networks, where the choice of layer types is randomized. The limited representational power of such models forces them to specialize rather than generalize, resulting in several distinct structural patterns. Observing the empirical performance of structurally diverse weaker models thus allows for some practical insight into the connection between the data and the choice of suitable CNN architectures.
    Keywords: Convolution ; Computational Modeling ; Training ; Kernel ; Data Models ; Complexity Theory ; Multi-Layer Neural Network ; Supervised Learning ; Pattern Analysis ; Knowledge Representation ; Applied Sciences
    E-ISSN: 2471-8963
    Source: IEEE Conference Publications
    Source: IEEE Xplore
    Source: IEEE Journals & Magazines 
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  • 9
    Language: English
    In: 2014 5th European Workshop on Visual Information Processing (EUVIP), December 2014, pp.1-6
    Description: Several existing content-based image retrieval and classification systems rely on low-level features which are automatically extracted from images. However, often these features lack the discrimination power needed for accurate description of the image content and hence they may lead to a poor retrieval or classification performance. This article applies an evolutionary feature synthesis method based on multi-dimensional particle swarm optimization on low-level image features to enhance their discrimination ability. The proposed method can be applied on any database and low-level features as long as some ground-truth information is available. Content-based image retrieval experiments show that a significant performance improvement can be achieved.
    Keywords: Vectors ; Feature Extraction ; Synthesizers ; Particle Swarm Optimization ; Training ; Databases ; Transforms ; Content-Based Image Retrieval ; Evolutionary Feature Synthesis ; Multi-Dimensional Particle Swarm Optimization
    Source: IEEE Conference Publications
    Source: IEEE Xplore
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  • 10
    Language: English
    In: Image and Vision Computing, October 2018, Vol.78, pp.73-83
    Description: Managing the water quality of freshwaters is a crucial task worldwide. One of the most used methods to biomonitor water quality is to sample benthic macroinvertebrate communities, in particular to examine the presence and proportion of certain species. This paper presents a benchmark database for automatic visual classification methods to evaluate their ability for distinguishing visually similar categories of aquatic macroinvertebrate taxa. We make publicly available a new database, containing 64 types of freshwater macroinvertebrates, ranging in number of images per category from 7 to 577. The database is divided into three datasets, varying in number of categories (64, 29, and 9 categories). Furthermore, in order to accomplish a baseline evaluation performance, we present the classification results of Convolutional Neural Networks (CNNs) that are widely used for deep learning tasks in large databases. Besides CNNs, we experimented with several other well-known classification methods using deep features extracted from the data.
    Keywords: Biomonitoring ; Fine-Grained Classification ; Benthic Macroinvertebrates ; Deep Learning ; Convolutional Neural Networks ; Engineering ; Applied Sciences
    ISSN: 0262-8856
    E-ISSN: 1872-8138
    Source: ScienceDirect Journals (Elsevier)
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