12,493 research outputs found
Visual-hint Boundary to Segment Algorithm for Image Segmentation
Image segmentation has been a very active research topic in image analysis
area. Currently, most of the image segmentation algorithms are designed based
on the idea that images are partitioned into a set of regions preserving
homogeneous intra-regions and inhomogeneous inter-regions. However, human
visual intuition does not always follow this pattern. A new image segmentation
method named Visual-Hint Boundary to Segment (VHBS) is introduced, which is
more consistent with human perceptions. VHBS abides by two visual hint rules
based on human perceptions: (i) the global scale boundaries tend to be the real
boundaries of the objects; (ii) two adjacent regions with quite different
colors or textures tend to result in the real boundaries between them. It has
been demonstrated by experiments that, compared with traditional image
segmentation method, VHBS has better performance and also preserves higher
computational efficiency.Comment: 45 page
Company2Vec -- German Company Embeddings based on Corporate Websites
With Company2Vec, the paper proposes a novel application in representation
learning. The model analyzes business activities from unstructured company
website data using Word2Vec and dimensionality reduction. Company2Vec maintains
semantic language structures and thus creates efficient company embeddings in
fine-granular industries. These semantic embeddings can be used for various
applications in banking. Direct relations between companies and words allow
semantic business analytics (e.g. top-n words for a company). Furthermore,
industry prediction is presented as a supervised learning application and
evaluation method. The vectorized structure of the embeddings allows measuring
companies similarities with the cosine distance. Company2Vec hence offers a
more fine-grained comparison of companies than the standard industry labels
(NACE). This property is relevant for unsupervised learning tasks, such as
clustering. An alternative industry segmentation is shown with k-means
clustering on the company embeddings. Finally, this paper proposes three
algorithms for (1) firm-centric, (2) industry-centric and (3) portfolio-centric
peer-firm identification.Comment: Accepted for Publication in: International Journal of Information
Technology & Decision Making (2023
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