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Spectral clustering is one of the most important image processing tools, especially for image segmentation. This specializes at taking local information such as edge weights and globalizing them. Due to its unsupervised nature, it is widely applicable. However, traditional spectral clustering is O(n 3/2). This poses a challenge, especially given the recent trend of large datasets. In this article, we propose an algorithm by using ideas from Γ −convergence, which is an amalgamation of Maximum Spanning Tree (MST) clustering and spectral clustering. This algorithm scales as O(nlog(n)) under certain conditions, while producing solutions which are similar to that of spectral clustering. Several toy examples are used to illustrate the similarities and differences. To validate the proposed algorithm, a recent state-of-the-art technique for segmentation-multiscale combinatorial grouping is used, where the normalized cut is replaced with the proposed algorithm and results are analyzed.

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Defining and measuring internationality as a function of influence diffusion of scientific journals is an open problem. There exists no metric to rank journals based on the extent or scale of internationality. Measuring internationality is qualitative, vague, open to interpretation and is limited by vested interests. With the tremendous increase in the number of journals in various fields and the unflinching desire of academics across the globe to publish in "international" journals, it has become an absolute necessity to evaluate, rank and categorize journals based on internationality. Authors, in the current work have defined internationality as a measure of influence that transcends across geographic boundaries. There are concerns raised by the authors about unethical practices reflected in the process of journal publication whereby scholarly influence of a select few are artificially boosted, primarily by resorting to editorial manoeuvres. To counter the impact of such tactics, authors have come up with a new method that defines and measures internationality by eliminating such local effects when computing the influence of journals. A new metric, Non-Local Influence Quotient (NLIQ) is proposed as one such parameter for internationality computation along with another novel metric, Other-Citation Quotient as the complement of the ratio of self-citation and total citation. In addition, SNIP and International Collaboration Ratio are used as two other parameters. As these journal parameters are not readily available in one place, algorithms to scrape these metrics are written and documented as a part of the current manuscript. Cobb-Douglas production function is utilized as a model to compute JIMI (Journal Internationality Modeling Index). Current work elucidates the metric acquisition algorithms while delivering arguments in favor of the suitability of the proposed model. Acquired data is corroborated by different supervised learning techniques. As part of future work, the authors present a bigger picture, RAGIS-Reputation And Global Influence Score, that will be computed to facilitate the formation of clusters of journals of high, moderate and low internationality.

Watershed technique from mathematical morphology (MM) is one of the most widely used operators for image segmentation. Recently watersheds are adapted to edge weighted graphs, allowing for wider applicability. However, a few questions remain to be answered-(a) How do the boundaries of the watershed operator behave? (b) Which loss function does the watershed operator optimize? (c) How does watershed operator relate with existing ideas from machine learning. In this article, a framework is developed, which allows one to answer these questions. This is achieved by generalizing the maximum margin principle to maximum margin partition and proposing a generic solution, MORPHMEDIAN, resulting in the maximum margin principle. It is then shown that watersheds form a particular class of MORPHMEDIAN classifiers. Using the ensemble technique, watersheds are also extended to ensemble watersheds. These techniques are compared with relevant methods from literature and it is shown that watersheds perform better than SVM on some datasets, and ensemble watersheds usually outperform random forest classifiers.