Digital images serve as the fundamental carrier for information exchange within multimedia ecosystems. However, the ubiquitous practice of screen recapturing often introduces complex moiré patterns due to spectral aliasing, which severely degrades the visual quality and impedes downstream multimedia analysis tasks. In this paper, we propose MoiréNet, a compact convolutional neural framework designed for effective moiré removal, thereby synergistically restoring high-fidelity content. To address the anisotropic and multi-scale nature of these artifacts, we introduce two novel modules: the Directional Frequency-Spatial Encoder (DFSE), which explicitly discerns moiré orientation via directional difference convolutions, and the Frequency-Spatial Adaptive Selector (FSAS), which enables feature-adaptive artifact suppression across dual domains. Extensive experiments demonstrate that MoiréNet achieves state-of-the-art performance on public and widely used datasets while being highly parameter-efficient. With only 5.513M parameters, representing a 48% reduction compared to ESDNet-L, MoiréNet combines superior restoration quality with parameter efficiency for storage-constrained multimedia applications.

Monochrome Near-Infrared (NIR) surveillance is widely used for low-light monitoring, yet pixel-level smoke segmentation remains underexplored due to scarce annotated data and strong appearance ambiguity. We introduce NIR-Smoke, a monochrome NIR smoke segmentation benchmark containing real frame-wise annotations and synthetic composites, and provide TrinitySeg as a strong baseline. Specifically, TrinitySeg integrates three complementary cues by capturing motion via lightweight frame differences, structure via multi-scale deep supervision with an auxiliary boundary head, and frequency via a compact Fourier-domain filtering module. We further study synthetic-to-real mixing for training. On NIR-Smoke, TrinitySeg achieves 85.64 ± 0.10 mIoU and 72.57 ± 0.21 Smoke IoU, outperforming representative baselines while retaining near-baseline inference cost.

There have been many attempts to classify imbalanced data, since this classification is critical in a wide variety of applications related to the detection of anomalies, failures, and risks. Many conventional methods, which can be categorized into sampling, cost-sensitive, or ensemble, include heuristic and task dependent processes. In order to achieve a better classification performance by formulation without heuristics and task dependence, we propose confusion-matrix-based kernel logistic regression (CM-KLOGR). Its objective function is the harmonic mean of various evaluation criteria derived from a confusion matrix, such criteria as sensitivity, positive predictive value, and others for negatives. This objective function and its optimization are consistently formulated on the framework of KLOGR, based on minimum classification error and generalized probabilistic descent (MCE/GPD) learning. Due to the merits of the harmonic mean, KLOGR, and MCE/GPD, CM-KLOGR improves the multifaceted performances in a well-balanced way. This paper presents the formulation of CM-KLOGR and its effectiveness through experiments that comparatively evaluated CM-KLOGR using benchmark imbalanced datasets.

Imbalanced data classification, which is a common and important problem in various fields related to the detection of anomaly, failure, and risk, has been studied intensively. Conventional methods are based on sampling, misclassification costs, or ensemble of classifiers, and many of them are heuristic and task dependent. Aiming at a higher classification performance with the solution of such problems, we propose a confusion-matrix-based kernel logistic regression (CM-KLOGR). After pretraining with a cross-entropy error function, CM-KLOGR retrains its parameters using the harmonic mean of the various evaluation criteria, including sensitivity, positive predictive value, and others, which are derived from a confusion matrix. CM-KLOGR inherits the advantages of kernel logistic regression, and it has the potential to raise the values of all the evaluation criteria in a well-balanced way. This paper presents the concept and formulation of CM-KLOGR, accompanied by the results of an exploratory experiment using an imbalanced biomedical dataset.

We propose a new method for automatically determining the smoothness of smooth classification error count loss for the recent Large Geometric Margin Minimum Classification Error (LGM-MCE) training. The method uses the Parzen-estimation-based formalization of MCE training, and it realizes the determination through the maximum likelihood estimation of error count risk in the one-dimensional geometric-margin-based misclassification measure. In the LGM-MCE framework, increase in the loss smoothness directly leads to an effect of producing virtual samples, which are expected to increase the training robustness to unseen samples. Focusing on this point, we also theoretically clarify the mechanism of this virtual sample generation. Through experiments, the utility of the proposed smoothness determination method is demonstrated, and the mechanism of producing virtual samples and its effect in robustness increase are also clearly illustrated.

Minimum Classification Error (MCE) training, which can be used to achieve minimum error classification of various types of patterns, has attracted a great deal of attention. However, to increase classification robustness, a conventional MCE framework has no practical optimization procedures like geometric margin maximization in Support Vector Machine (SVM). To realize high robustness in a wide range of classification tasks, we derive the geometric margin for a general class of discriminant functions and develop a new MCE training method that increases the geometric margin value. We also experimentally demonstrate the effectiveness of our new method using prototype-based classifiers.

Interactive EC Fitting is an effective fitting method to set the signal processing parameters of a hearing aid reflecting the preference of each user. However, it sometimes generates uncomfortable sounds and takes long fitting time. This paper proposes an improvement of Interactive EC Fitting by embedding a function that screens out a subset of candidate settings which are predicted to cause uncomfortable sounds by using sound volume preference. We evaluated the improved method through a simulation-based experiment. The results statistically showed that the improved method reduces the variance of evaluation values of candidate settings and accelerates the EC convergence, compared to original Interactive EC Fitting. It was therefore indicated that the improved method is effective in screening uncomfortable sounds and shortening fitting time. We will confirm the actual effectiveness of the improved method by conducting psychological experiments in our future work.

Subsequence time-series clustering (STSC), which consists of subsequence cutout with a sliding window and k-means clustering, had been commonly used in time-series data mining. However, a problem was pointed out that STSC always generates moderate sinusoidal patterns independently of the input. To address this problem, we theoretically explain and empirically confirm the similarity between STSC and moving average. The present analysis is consistent with, and simpler than, one of the most important analyses of STSC. We also question the pattern extraction in the time domain and discuss another solution.

In this paper, we present an evaluation of learning algo- rithms of a novel rule evaluation support method for post- processing of mined results with rule evaluation models based on objective indices. Post-processing of mined results is one of the key processes in a data mining process. How- ever, it is difficult for human experts to completely evaluate several thousands of rules from a large dataset with noises. To reduce the costs in such rule evaluation task, we have de- veloped the rule evaluation support method with rule evalu- ation models, which learn from objective indices for mined classification rules and evaluations by a human expert for each rule. To enhance adaptability of rule evaluation mod- els, we introduced a constructive meta-learning system to choose proper learning algorithms. Then, we have done the case study on the meningitis data mining as an actual prob- lem.

An interactive evolutionary computation (EC) fitting method is proposed that applies interactive EC to hearing aid fitting and the method is evaluated using a hearing aid simulator with human subjects. The advantages of the method are that it can optimize a hearing aid based on how a user hears and that it realizes whatever+whenever+wherever (W3) fitting. Conventional fitting methods are based on the user's partially measured auditory characteristics, the fitting engineer's experience, and the user's linguistic explanation of his or her hearing. These conventional methods, therefore, suffer from the fundamental problem that no one can experience another person's hearing. However, as interactive EC fitting uses EC to optimize a hearing aid based on the user's evaluation of his or her hearing, this problem is addressed. Moreover, whereas conventional fitting methods must use pure tones and bandpass noise for measuring hearing characteristics, our proposed method has no such restrictions. Evaluating the proposed method using speech sources, we demonstrate that it shows significantly better results than either the conventional method or the unprocessed case in terms of both speech intelligibility and speech quality. We also evaluate our method using musical sources, unusable for evaluation by conventional methods, and demonstrate that its sound quality is preferable to the unprocessed case

In this paper, we present a novel rule evaluation support method for post-processing of mined results with rule evaluation models based on objective indices. Post-processing of mined results is one of the key issues to make a data mining process successfully. However, it is difficult for human experts to evaluate many thousands of rules from a large dataset with noises completely. To reduce the costs of rule evaluation procedures, we have developed the rule evaluation support method with rule evaluation models, which are obtained with objective indices of mined classification rules and evaluations of a human expert for each rule. To evaluate performances of learning algorithms for constructing rule evaluation models, we have done a case study on the meningitis data mining as an actual problem. Then we discuss the availability of our rule evaluation support method.

In this paper, we present a novel rule evaluation support method for post-processing of mined results with rule evaluation models based on objective indexes. Post-processing of mined results is one of the key issues to make a data mining process successfully. However, it is difficult for human experts to evaluate many thousands of rules from a large dataset with noises completely. To reduce the costs of rule evaluation procedures, we have developed the rule evaluation support method with rule evaluation models, which are obtained with objective rule evaluation indexes and evaluations of a human expert for each rule. Since the method is needed more accurate rule evaluation models, we have compared learning algorithms to construct rule evaluation models with the actual meningitis data mining result and actual rule sets from UCI datasets. Then we show the availability of our adaptive rule evaluation support method.

We propose a method to quantitatively evaluate image processing methods on how well they simulate human visual processing through psychological and statistical tests. First, we focus on edge detection, an image processing technique. To set all edge detection methods fairly, we collect the edge data detected by humans. Then, the signal processing parameters for each edge detection method are adjusted with the same optimizing method based on minimizing the error between the edges detected by the detector and by humans. Two psychological experiments are conducted, confirming whether the edge detection parameters are optimized and evaluating the performance of edge detection methods. The results indicate that the optimizing method functioned comparatively well. The effectiveness rank of edge detection methods is also obtained.

This paper proposes to display individuals of interactive evolutionary computations in an evaluation order to reduce the burden on human operators. To display an evaluation order, two prediction methods-the first using neural networks, and the second using Euclidean distance measure-are proposed. We evaluate their predictive performance through simulation experiments and subjective tests.