The PASCAL Visual Object Classes Challenge

https://scholarlypublications.universiteitleiden.nl/access/item%3A2943283/view

Deep learning for visual understanding

This study aimed to review the literature describing and quantifying time lags in the health research translation process. Papers were included in the review if they quantified time lags in the development of health interventions. The study identified 23 papers. Few were comparable as different studies use different measures, of different things, at different time points. We concluded that the current state of knowledge of time lags is of limited use to those responsible for R&D and knowledge transfer who face difficulties in knowing what they should or can do to reduce time lags. This effectively ‘blindfolds’ investment decisions and risks wasting effort. The study concludes that understanding lags first requires agreeing models, definitions and measures, which can be applied in practice. A second task would be to develop a process by which to gather these data.

The Answer Is 17 Years, What Is the Question

Learning Deconvolution Network for Semantic Segmentation

We introduce hand movement, orientation, and grasp (HMOG), a set of behavioral features to continuously authenticate smartphone users. HMOG features unobtrusively capture subtle micro-movement and orientation dynamics resulting from how a user grasps, holds, and taps on the smartphone. We evaluated authentication and biometric key generation (BKG) performance of HMOG features on data collected from 100 subjects typing on a virtual keyboard. Data were collected under two conditions: 1) sitting and 2) walking. We achieved authentication equal error rates (EERs) as low as 7.16% (walking) and 10.05% (sitting) when we combined HMOG, tap, and keystroke features. We performed experiments to investigate why HMOG features perform well during walking. Our results suggest that this is due to the ability of HMOG features to capture distinctive body movements caused by walking, in addition to the hand-movement dynamics from taps. With BKG, we achieved the EERs of 15.1% using HMOG combined with taps. In comparison, BKG using tap, key hold, and swipe features had EERs between 25.7% and 34.2%. We also analyzed the energy consumption of HMOG feature extraction and computation. Our analysis shows that HMOG features extracted at a 16-Hz sensor sampling rate incurred a minor overhead of 7.9% without sacrificing authentication accuracy. Two points distinguish our work from current literature: 1) we present the results of a comprehensive evaluation of three types of features (HMOG, keystroke, and tap) and their combinations under the same experimental conditions and 2) we analyze the features from three perspectives (authentication, BKG, and energy consumption on smartphones).

/pdf/hmog-new-behavioral-biometric-features-for-continuous-wxqxi7t4mv.pdf

HMOG: New Behavioral Biometric Features for Continuous Authentication of Smartphone Users

In this paper, we propose a bottom-up approach to generating short descriptive sentences from images, to enhance scene understanding. We demonstrate automatic methods for mapping the visual content in an image to natural spoken or written language. We also introduce a human-in-the-loop evaluation strategy that quantitatively captures the meaningfulness of the generated sentences. We recorded a correctness rate of 60.34% when human users were asked to judge the meaningfulness of the sentences generated from relatively challenging images. Also, our automatic methods compared well with the state-of-the-art techniques for the related computer vision tasks.

DISCO: describing images using scene contexts and objects

This paper investigates whether the cognitive state of a person can be learnt and used as a novel biometric trait. We explore the idea of using language written by an author, as his/her cognitive fingerprint. The dataset consists of millions of blogs written by thousands of authors on the Internet. Our proposed method learns a classifier that can distinguish between genuine and impostor authors. Our results are encouraging (we report 72% Area under the ROC curve) and show that users do have a distinctive linguistic style, which is evident even when analyzing a corpora as large and diverse as the Internet. When we tested on new authors that the system had never encountered before, our methodology correctly identified genuine authors with 78% accuracy and impostors with 76% accuracy.

Use of language as a cognitive biometric trait

Gesture sequences typically have a common set of distinct internal sub-structures which can be shared across the gestures. In this paper, we propose a method using a generative model to learn these common actions which we refer to as sub-gestures, and in-turn perform recognition. Our proposed model learns sub-gestures by sharing parameters between gesture models. We evaluated our method on the Palm Graffiti digits-gesture dataset and showed that the model with shared parameters outperformed the same model without the shared parameters. Also, we labeled different observation sequences thereby intuitively showing how sub-gestures are related to complete gestures.

A shared parameter model for gesture and sub-gesture analysis

We present an ontology-guided, symbol-based, image parser which involves the use of semantic, spoken language descriptions of entities in images as well as the real-world spatial relationships defined between these entities. Our parsing approach explicitly describes objects and the relationships between them with linguistically meaningful modes of colors, textures and [coarse] expressions of shapes. The image parser is built on a syntactic image grammar-based framework and performs a (near) global optimization using superpixels as an initial set of subpatterns. It hypothesizes the entities in images using their local semantic attributes and verifies them globally using their more global features and their relative spatial locations,. Evaluations of the parser are performed on selected images which we make publicly available along with their manual segmentations and our labeling results.

Syntactic image parsing using ontology and semantic descriptions

In this paper we present a prototype for an automated deception detection system. Similar to polygraph examinations, we
attempt to take advantage of the theory that false answers will produce distinctive measurements in certain physiological
manifestations. We investigate the role of dynamic eye-based features such as eye closure/blinking and lateral movements
of the iris in detecting deceit. The features are recorded both when the test subjects are having non-threatening conversations
as well as when they are being interrogated about a crime they might have committed. The rates of the behavioral
changes are blindly clustered into two groups. Examining the clusters and their characteristics, we observe that the dynamic
features selected for deception detection show promising results with an overall deceptive/non-deceptive prediction
rate of 71.43% from a study consisting of 28 subjects.

Ifeoma Nwogu

Papers

DISCO: describing images using scene contexts and objects

Use of language as a cognitive biometric trait

A shared parameter model for gesture and sub-gesture analysis

Syntactic image parsing using ontology and semantic descriptions

An automated process for deceit detection