We have witnessed great interest and a wealth of promise in content-based image retrieval as an emerging technology. While the last decade laid foundation to such promise, it also paved the way for a large number of new techniques and systems, got many new people involved, and triggered stronger association of weakly related fields. In this article, we survey almost 300 key theoretical and empirical contributions in the current decade related to image retrieval and automatic image annotation, and in the process discuss the spawning of related subfields. We also discuss significant challenges involved in the adaptation of existing image retrieval techniques to build systems that can be useful in the real world. In retrospect of what has been achieved so far, we also conjecture what the future may hold for image retrieval research.

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Image retrieval: Ideas, influences, and trends of the new age

The PASCAL Visual Object Classes Challenge

A survey of content-based image retrieval with high-level semantics

Exploring and identifying structure is even more important for multivariate data than univariate data, given the difficulties in graphically presenting multivariate data and the comparative lack of parametric models to represent it. Unfortunately, such exploration is also inherently more difficult.

Multivariate Density Estimation

▶ Addresses the accessibility, usability, and, ultimately, acceptability of Information Society Technologies by anyone, anywhere, at anytime, and through any media and device. ▶ Focuses on theoretical, methodological, and empirical research, of both technological and non-technological nature. ▶ Features papers that report on theories, methods, tools, empirical results, reviews, case studies, and best-practice examples.

Universal access in the information society

On the new robust algorithm of noise reduction in color images

Detection of deceptive facial expressions, including estimating smile genuineness, is an important and challenging research topic that draws increasing attention from the computer vision and pattern recognition community. The state-of-the-art methods require localizing a number of facial landmarks to extract sophisticated facial characteristics. In this paper, we explore how to exploit fast smile intensity detectors to extract temporal features. This allows for real-time discrimination between posed and spontaneous expressions at the early smile onset phase. We report the results of experimental validation, which indicate high competitiveness of our method for the UvA-NEMO benchmark database.

In Search of Truth: Analysis of Smile Intensity Dynamics to Detect Deception

In this paper a new approach to the problem of impulsive noise removal in color images is presented The proposed method is based on the evaluation of the statistical properties of a sorted sequence of the accumulated distances used for the calculation of the vector median of samples belonging to the filtering window The statistical analysis is performed using the Fisher's linear discriminant, which enables the detection of outliers introduced by the noise process The described filter output switches between the vector median and the original central pixel In order to increase the filter's performance, two thresholds are introduced, which enhance the detail preserving abilities of the proposed filtering scheme The described filtering technique is robust, fast and therefore it can be used in real time denoising applications

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Peer Group Filter for Impulsive Noise Removal in Color Images

Applying computer vision techniques to distinguish between spontaneous and posed smiles is an active research topic of affective computing. Although there have been many works published addressing this problem and a couple of excellent benchmark databases created, the existing state-of-the-art approaches do not exploit the action units defined within the Facial Action Coding System that has become a standard in facial expression analysis. In this work, we explore the possibilities of extracting discriminative features directly from the dynamics of facial action units to differentiate between genuine and posed smiles. We report the results of our experimental study which shows that the proposed features offer competitive performance to those based on facial landmark analysis and on textural descriptors extracted from spatial-temporal blocks. We make these features publicly available for the UvA-NEMO and BBC databases, which will allow other researchers to further improve the classification scores, while preserving the interpretation capabilities attributed to the use of facial action units. Moreover, we have developed a new technique for identifying the smile phases, which is robust against the noise and allows for continuous analysis of facial videos.

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Dynamics of facial actions for assessing smile genuineness.

In this paper a new approach to the problem of contrast enhancement of gray scale images is presented. The algorithms introduced here are based on a model of a virtual particle, which performs a random walk on the image lattice. It is assumed, that the probability of a transition of the walking particle from a lattice point to a point belonging to its neighbourhood is determined by the Gibbs distribution, defined on a specified neighbourhood system.

In this work four algorithms of contrast enhancement are presented. The first algorithm traces the visits of the walking particle and determines their relative frequencies. The second operator assigns to each lattice point the probability of a stationary Markov chain, generated by the trajectory of the randomly walking particle. The third algorithm is based on a concept of a jumping particle and the last one uses the information contained in the statistical sum of the Gibbs distribution of the transition probabilities.

Bogdan Smolka

Papers

On the new robust algorithm of noise reduction in color images

In Search of Truth: Analysis of Smile Intensity Dynamics to Detect Deception

Peer Group Filter for Impulsive Noise Removal in Color Images

Dynamics of facial actions for assessing smile genuineness.

Contrast Enhancement of Gray Scale Images Based on the Random Walk Model