Traditionally, research on visual attention has been focused on the processes involved in conscious, explicit selection of task-relevant sensory input. Recently, however, it has been shown that attending to a specific feature of an object automatically increases neural sensitivity to this feature throughout the visual field. Here we show that directing attention to a specific color of an object...

In this paper we present our approach to the 2011 ImageClef PhotoAnnotation task, which is based on the well known bag-of-words model. We investigated an approach for selecting the most informative training samples per concept for classification and the impact of fusing the OpponentSIFT feature with the GIST feature which calculates global image statistics, on scene-based concepts. We also inco...

When dealing with subjective, noisy, or otherwise nebulous features, the “wisdom of crowds” suggests that one may benefit from multiple judgments of the same feature on the same object. We give theoreticallymotivated feature multi-selection algorithms that choose, among a large set of candidate features, not only which features to judge but how many times to judge each one. We demonstrate the e...

Beginning in the first century AD, Buddhist texts underwent a series of translations during a period of nearly 1300 years. The identification of the translator, textual apocrypha, and translation style in Buddhist texts are always important issues. This study proposes an approach to find the most discriminative features that characterize the different Buddhist translation texts or other transla...

Currently, the best detectors of content-adaptive steganography are built as classifiers trained on examples of cover and stego images represented with rich media models (features) formed by histograms (or co-occurrences) of quantized noise residuals. Recently, it has been shown that adaptive steganography can be more accurately detected by incorporating content adaptivity within the features b...