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Abstract

The techniques of clustering and space transformation have been successfully used in the past to solve a number of pattern recognition problems. In this article, the authors propose a new approach to content-based image retrieval (CBIR) that uses (a) a newly proposed similarity-preserving space transformation method to transform the original low-level image space into a highlevel vector space that enables efficient query processing, and (b) a clustering scheme that further improves the efficiency of our retrieval system. This combination is unique and the resulting system provides synergistic advantages of using both clustering and space transformation. The proposed space transformation method is shown to preserve the order of the distances in the transformed feature space. This strategy makes this approach to retrieval generic as it can be applied to object types, other than images, and feature spaces more general than metric spaces. The CBIR approach uses the inexpensive "estimated" distance in the transformed space, as opposed to the computationally inefficient "real" distance in the original space, to retrieve the desired results for a given query image. The authors also provide a theoretical analysis of the complexity of their CBIR approach when used for color-based retrieval, which shows that it is computationally more efficient than other comparable approaches. An extensive set of experiments to test the efficiency and effectiveness of the proposed approach has been performed. The results show that the approach offers superior response time (improvement of 1-2 orders of magnitude compared to retrieval approaches that either use pruning techniques like indexing, clustering, etc., or space transformation, but not both) with sufficiently high retrieval accuracy.

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Abstract

This study examines trends in open access article processing charges (APCs) from 2011 to 2021, building on a 2011 study by Solomon and Björk. Two methods are employed, a modified replica and a status update of the 2011 journals. Data are drawn from multiple sources and datasets are available as open data. Most journals do not charge APCs; this has not changed. The global average per-journal APC increased slightly, from 906 to 958 USD, while the per-article average increased from 904 to 1,626 USD, indicating that authors choose to publish in more expensive journals. Publisher size, type, impact metrics and subject affect charging tendencies, average APC, and pricing trends. Half the journals from the 2011 sample are no longer listed in DOAJ in 2021, due to ceased publication or publisher de-listing. Conclusions include a caution about the potential of the APC model to increase costs beyond inflation. The university sector may be the most promising approach to economically sustainable no-fee OA journals. Universities publish many OA journals, nearly half of OA articles, tend not to charge APCs and when APCs are charged, the prices are very low on average.

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