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1

Metric correctness of pairwise comparisons in intelligent data analysis

Dvoenko Sergey D.

Control and Cybernetics

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2023

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Vol. 52, No. 3

291--334

EN

In modern data analysis and machine learning, data are often represented in the form of pairwise comparisons of the elements of the data set. The pairwise comparisons immediately correspond to the similarity or dissimilarity of objects under investigation, and such a situation regularly arises in the domains of image and signal analysis, bioinformatics, expert evaluation, etc. The practical pairwise comparison functions may be incorrect in terms of potentially using them as scalar products or distances. In contrast to other approaches, we develop in this paper a technique based on the so-called metric approach, which proposes to modify the values of empirical functions so as to get scalar products or distances. The methods for obtaining the correct matrices of pairwise comparisons and for improving their conditionality are developed here.

2

Image mining based on deep belief neural networkand feature matching approachusing manhattan distance

Ahmad Faiyaz, Ahmad Tanvir

Computer Assisted Methods in Engineering and Science

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2021

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Vol. 28, no. 2

139--167

EN

Over the past few decades multimedia content, particularly digital images, has increased at a rapid pace, with several complex images being uploaded to various social websites such as Instagram, Facebook and Twitter. Therefore, it is difficult to search and retrieve the relevant image in seconds. Search engines retrieve images based on traditional text-based methods that depend on metadata and captions. In the last few years, a wide range of research has focused on content-based image retrieval (CBIR) based on image mining approaches. This is a challenging research area due to the ever-increasing multimedia database and other image libraries. In order to offer an effective search and retrieval, a novel CBIR system is proposed using the image mining-based deep belief neural network (IMDBN) technique. The proposed method is designed to enhance retrieval accuracy while diminishing the semantic gap between human visual understanding and image feature representation. To achieve this objective, the proposed system carries several steps like preprocessing, feature extraction, classification, and feature matching. Initially, the input database images are fed into the proposed image mining-based CBIR system, whereas colour-shape-texture (CST) feature extraction technique is applied to extract relevant feature set. The extracted features are fused and stored in the feature vector and are subjected to the proposed IMDBN classification step to retrieve similar images in one label. Whenever a new query content is created, the most relevant images are retrieved. This, in turn, achieves 94% accuracy, which is higher than in existing approaches.

3

Multiple dichotomies in timbre research

Papanikolaou G., Pastiadis C.

Archives of Acoustics

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2009

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Vol. 34, No. 2

137-155

EN

In this paper an overview of aspects, terminology and literature on contemporary research regarding timbre is presented. Timbre is a multidimensional entity, and research traces its multifaceted nature. The paper handles this structural complexity using a domain-task-results paradigm. Several domains of application are examined and various aspects of timbre questioning are outlined, although consideration of aspects in music and its contextual applications are postponed for a following detailed report for reasons of presentation compactness and extent. A self-evident differentiation of research categorization stems from the type of consideration of timbre as a perceptual attribute or as a manifestation of physical (either generative or modified after transmission) phenomena and processes. As more "axes" of differentiation also emerge, this work attempts to highlight issues that rise and propose possible research directions.