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Efficient Image Retrieval by Fuzzy Rules from Boosting and Metaheuristic

Abstract

Fast content-based image retrieval is still a challenge for computer systems. We present a novel method aimed at classifying images by fuzzy rules and local image features. The fuzzy rule base is generated in the first stage by a boosting procedure. Boosting meta-learning is used to find the most representative local features. We briefly explore the utilization of metaheuristic algorithms for the various tasks of fuzzy systems optimization. We also provide a comprehensive description of the current best-performing DISH algorithm, which represents a powerful version of the differential evolution algorithm with effective embedded mechanisms for stronger exploration and preservation of the population diversity, designed for higher dimensional and complex optimization tasks. The algorithm is used to fine-tune the fuzzy rule base. The fuzzy rules can also be used to create a database index to retrieve images similar to the query image fast. The proposed approach is tested on a state-of-the-art image dataset and compared with the bag-of-features image representation model combined with the Support Vector Machine classification. The novel method gives a better classification accuracy, and the time of the training and testing process is significantly shorter.

Open access
On-Line Signature Partitioning Using a Population Based Algorithm

Abstract

The on-line signature is a biometric attribute which can be used for identity verification. It is a very useful characteristic because it is commonly accepted in societies across the world. However, the verification process using this particular biometric feature is a rather difficult one. Researchers working on identity verification involving the on-line signature might face various problems, including the different discriminative power of signature descriptors, the problem of a large number of descriptors, the problem of descriptor generation, etc. However, population-based algorithms (PBAs) can prove very useful when resolving these problems. Hence, we propose a new method for on-line signature partitioning using a PBA in order to improve the verification process effectiveness. Our method uses the Differential Evolution algorithm with a properly defined evaluation function for creating the most characteristic partitions of the dynamic signature. We present simulation results of the proposed method for the BioSecure DS2 database distributed by the BioSecure Association.

Open access
On Training Deep Neural Networks Using a Streaming Approach

Abstract

In recent years, many deep learning methods, allowed for a significant improvement of systems based on artificial intelligence methods. Their effectiveness results from an ability to analyze large labeled datasets. The price for such high accuracy is the long training time, necessary to process such large amounts of data. On the other hand, along with the increase in the number of collected data, the field of data stream analysis was developed. It enables to process data immediately, with no need to store them. In this work, we decided to take advantage of the benefits of data streaming in order to accelerate the training of deep neural networks. The work includes an analysis of two approaches to network learning, presented on the background of traditional stochastic and batch-based methods.

Open access
Rough Support Vector Machine for Classification with Interval and Incomplete Data

Abstract

The paper presents the idea of connecting the concepts of the Vapnik’s support vector machine with Pawlak’s rough sets in one classification scheme. The hybrid system will be applied to classifying data in the form of intervals and with missing values [1]. Both situations will be treated as a cause of dividing input space into equivalence classes. Then, the SVM procedure will lead to a classification of input data into rough sets of the desired classes, i.e. to their positive, boundary or negative regions. Such a form of answer is also called a three–way decision. The proposed solution will be tested using several popular benchmarks.

Open access
A Strong and Efficient Baseline for Vehicle Re-Identification Using Deep Triplet Embedding

Abstract

In this paper we tackle the problem of vehicle re-identification in a camera network utilizing triplet embeddings. Re-identification is the problem of matching appearances of objects across different cameras. With the proliferation of surveillance cameras enabling smart and safer cities, there is an ever-increasing need to re-identify vehicles across cameras. Typical challenges arising in smart city scenarios include variations of viewpoints, illumination and self occlusions. Most successful approaches for re-identification involve (deep) learning an embedding space such that the vehicles of same identities are projected closer to one another, compared to the vehicles representing different identities. Popular loss functions for learning an embedding (space) include contrastive or triplet loss. In this paper we provide an extensive evaluation of triplet loss applied to vehicle re-identification and demonstrate that using the recently proposed sampling approaches for mining informative data points outperform most of the existing state-of-the-art approaches for vehicle re-identification. Compared to most existing state-of-the-art approaches, our approach is simpler and more straightforward for training utilizing only identity-level annotations, along with one of the smallest published embedding dimensions for efficient inference. Furthermore in this work we introduce a formal evaluation of a triplet sampling variant (batch sample) into the re-identification literature. In addition to the conference version [24], this submission adds extensive experiments on new released datasets, cross domain evaluations and ablation studies.

Open access
Characterization of Surface Micro-Roughness by Off-Specular Measurements of Polarized Optical Scattering

Abstract

The characterization of surface micro-roughness is investigated by using off-specular measurements of polarized optical scattering. In the measurement system, the detection angles of optical scattering are defined by the vertical and level scattering angles. The rotating mechanism of angles is controlled by stepper motors. Waveplate and polarizer are used to adjust light polarization and detection. We conduct the optical scattering measurements by using four standard metal sheets of surface roughness. The nominal values (Ra) of standard micro-roughness are 1.6 μm, 0.8 μm, 0.4 μm, and 0.1 μm, respectively. Samples with different surface roughness are evaluated with the utilization of laser sources at three incident wavelengths. These polarized images are analyzed using a computer program to obtain the distribution of light intensity. The results show great correlation between the metal surface roughness and polarization states. This measurement system can be used to quickly and accurately distinguish between different surfaces and properties.

Open access
Detection of Deterioration of Three-phase Induction Motor using Vibration Signals

Abstract

Nowadays detection of deterioration of electrical motors is an important topic of research. Vibration signals often carry diagnostic information of a motor. The authors proposed a setup for the analysis of vibration signals of three-phase induction motors. In this paper rotor fault diagnostic techniques of a three-phase induction motor (TPIM) were presented. The presented techniques used vibration signals and signal processing methods. The authors analyzed the recognition rate of vibration signal readings for 3 states of the TPIM: healthy TPIM, TPIM with 1 broken bar, and TPIM with 2 broken bars. In this paper the authors described a method of the feature extraction of vibration signals Method of Selection of Amplitudes of Frequencies – MSAF-12. Feature vectors were obtained using FFT, MSAF-12, and mean of vector sum. Three methods of classification were used: Nearest Neighbor (NN), Linear Discriminant Analysis (LDA), and Linear Support Vector Machine (LSVM). The obtained results of analyzed classifiers were in the range of 97.61 % – 100 %.

Open access
Interlaboratory Comparison of Thermal AC Voltage Standards

Abstract

The article presents results of comparison of the thermal converter of nominal input voltages equal to 10 V from the set of Polish National AC voltage standards, maintained at the Central Office of Measures in Warsaw, with the primary AC voltage 5 V standard, developed and maintained at the AC-DC Transfer Laboratory of the Department of Measurement Science, Electronics and Control at the Faculty of Electrical Engineering of the Silesian University of Technology in Gliwice.

Open access
Measurement of Maximum Deviation from Roundness Based on the Inverse Kinematics Principle

Abstract

The article deals with a special method of measuring the maximum deviation of objects from roundness based on the inverse kinematics principle. The inverse measurement mechanism is based on the immobility of the measuring probes and the object performing all the motions required to measure a dimension. The advantage of this principle is minimization of the temperature change, while the adverse effect in the measurement system is greatly reduced at the same time. The measurement methodology requires a special software evaluation of the data measured. The aim of the given measurement methodology was to establish the maximum roundness deviation that corresponds to the Least Squares Circle (LSC) method. An experiment with three measuring probes was conducted to verify the methodology.

Open access
Measuring the Moment and the Magnitude of the Abrupt Change of the Gaussian Process Bandwidth

Abstract

The maximum likelihood algorithm is introduced for measuring the unknown moment of abrupt change and bandwidth jump of a fast-fluctuating Gaussian random process. This algorithm can be technically implemented much simpler than the ones obtained by means of common approaches. The technique for calculating the characteristics of the synthesized measurer is presented and the closed analytical expressions for the conditional biases and variances of the resulting estimates are found using the additive local Markov approximation of the decision statistics. By statistical simulation methods, it is confirmed that the presented measurer is operable, while the theoretical formulas describing its performance well approximate the corresponding experimental data in a wide range of the parameter values of the analyzed random process.

Open access