Parameterized Leaf Power Recognition via Embedding into Graph Products
نویسندگان
چکیده
منابع مشابه
Generic object recognition using graph embedding into a vector space
This paper describes a method for generic object recognition using graph structural expression. In recent years, generic object recognition by computer is finding extensive use in a variety of fields, including robotic vision and image retrieval. Conventional methods use a bag-of-features (BoF) approach, which expresses the image as an appearance frequency histogram of visual words by quantizin...
متن کاملGraph-embedding for speaker recognition
Popular methods for speaker classification perform speaker comparison in a high-dimensional space [1], however, recent work [2] has shown that most of the speaker variability is captured by a low-dimensional subspace of that space. In this paper we examine whether additional structure in terms of nonlinear manifolds exist within the high-dimensional space. We will use graph embedding [3] as a p...
متن کاملGraph Augmentation via Metric Embedding
Kleinberg [17] proposed in 2000 the first random graph model achieving to reproduce small world navigability, i.e. the ability to greedily discover polylogarithmic routes between any pair of nodes in a graph, with only a partial knowledge of distances. Following this seminal work, a major challenge was to extend this model to larger classes of graphs than regular meshes, introducing the concept...
متن کاملGraph Kernels via Functional Embedding
We propose a representation of graph as a functional object derived from the power iteration of the underlying adjacency matrix. The proposed functional representation is a graph invariant, i.e., the functional remains unchanged under any reordering of the vertices. This property eliminates the difficulty of handling exponentially many isomorphic forms. Bhattacharyya kernel constructed between ...
متن کاملEmbedding Partially Ordered Sets into Chain-products
Embedding partially ordered sets into chain-products is already known to be NP-complete (see Yannakakis 30] for dimension or Stahl and Wille 26] for 2-dimension). In this paper, we introduce a new dimension parameter and show that encoding using terms (or k-dimension) is not better than bit-vector (or 2-dimension) and vice versa. A decomposition theory is introduced using coatomic lattices. An ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Algorithmica
سال: 2020
ISSN: 0178-4617,1432-0541
DOI: 10.1007/s00453-020-00720-8