Does rated visual landscape quality match visual features? An analysis for renewable energy landscapes

Efficient Match Kernel between Sets of Features for Visual Recognition

In visual recognition, the images are frequently modeled as unordered collections of local features (bags). We show that bag-of-words representations commonly used in conjunction with linear classifiers can be viewed as special match kernels, which count 1 if two local features fall into the same regions partitioned by visual words and 0 otherwise. Despite its simplicity, this quantization is t...

Efficient Match Kernels between Sets of Features for Visual Recognition

In visual recognition, the images are frequently modeled as unordered collections of local features (bags). We show that bag-of-words representations commonly used in conjunction with linear classifiers can be viewed as special match kernels, which count 1 if two local features fall into the same regions partitioned by visual words and 0 otherwise. Despite its simplicity, this quantization is t...

Spatiotemporal Oriented Energy Features for Visual Tracking

This paper presents a novel feature set for visual tracking that is derived from “oriented energies”. More specifically, energy measures are used to capture a target’s multiscale orientation structure across both space and time, yielding a rich description of its spatiotemporal characteristics. To illustrate utility with respect to a particular tracking mechanism, we show how to instantiate ori...

Coverage Quality in Visual Sensor Networks

Quality-aware visual data analysis

The quality, certainty, or confidence of decisions made during the visual analytics process depends on many factors, including the completeness and reliability of the initial data, information loss due to filtering, sampling, and other transformations, and the accuracy and clarity of the visual presentation. Unfortunately, in most visualization tools the analyst is unaware of these and other fo...