19,522 research outputs found
Semantic spaces revisited: investigating the performance of auto-annotation and semantic retrieval using semantic spaces
Semantic spaces encode similarity relationships between objects as a function of position in a mathematical space. This paper discusses three different formulations for building semantic spaces which allow the automatic-annotation and semantic retrieval of images. The models discussed in this paper require that the image content be described in the form of a series of visual-terms, rather than as a continuous feature-vector. The paper also discusses how these term-based models compare to the latest state-of-the-art continuous feature models for auto-annotation and retrieval
The Algebraic Approach to Phase Retrieval and Explicit Inversion at the Identifiability Threshold
We study phase retrieval from magnitude measurements of an unknown signal as
an algebraic estimation problem. Indeed, phase retrieval from rank-one and more
general linear measurements can be treated in an algebraic way. It is verified
that a certain number of generic rank-one or generic linear measurements are
sufficient to enable signal reconstruction for generic signals, and slightly
more generic measurements yield reconstructability for all signals. Our results
solve a few open problems stated in the recent literature. Furthermore, we show
how the algebraic estimation problem can be solved by a closed-form algebraic
estimation technique, termed ideal regression, providing non-asymptotic success
guarantees
Automated Generation of Geometric Theorems from Images of Diagrams
We propose an approach to generate geometric theorems from electronic images
of diagrams automatically. The approach makes use of techniques of Hough
transform to recognize geometric objects and their labels and of numeric
verification to mine basic geometric relations. Candidate propositions are
generated from the retrieved information by using six strategies and geometric
theorems are obtained from the candidates via algebraic computation.
Experiments with a preliminary implementation illustrate the effectiveness and
efficiency of the proposed approach for generating nontrivial theorems from
images of diagrams. This work demonstrates the feasibility of automated
discovery of profound geometric knowledge from simple image data and has
potential applications in geometric knowledge management and education.Comment: 31 pages. Submitted to Annals of Mathematics and Artificial
Intelligence (special issue on Geometric Reasoning
- …