RESUMO
This paper uses quantitative eye tracking indicators to analyze the relationship between images of paintings and human viewing. First, we build the eye tracking fixation sequences through areas of interest (AOIs) into an information channel, the gaze channel. Although this channel can be interpreted as a generalization of a first-order Markov chain, we show that the gaze channel is fully independent of this interpretation, and stands even when first-order Markov chain modeling would no longer fit. The entropy of the equilibrium distribution and the conditional entropy of a Markov chain are extended with additional information-theoretic measures, such as joint entropy, mutual information, and conditional entropy of each area of interest. Then, the gaze information channel is applied to analyze a subset of Van Gogh paintings. Van Gogh artworks, classified by art critics into several periods, have been studied under computational aesthetics measures, which include the use of Kolmogorov complexity and permutation entropy. The gaze information channel paradigm allows the information-theoretic measures to analyze both individual gaze behavior and clustered behavior from observers and paintings. Finally, we show that there is a clear correlation between the gaze information channel quantities that come from direct human observation, and the computational aesthetics measures that do not rely on any human observation at all.
RESUMO
Riding the wave of visual sensor equipment (e.g., personal smartphones, home security cameras, vehicle cameras, and camcorders), image retrieval (IR) technology has received increasing attention due to its potential applications in e-commerce, visual surveillance, and intelligent traffic. However, determining how to design an effective feature descriptor has been proven to be the main bottleneck for retrieving a set of images of interest. In this paper, we first construct a six-layer color quantizer to extract a color map. Then, motivated by the human visual system, we design a local parallel cross pattern (LPCP) in which the local binary pattern (LBP) map is amalgamated with the color map in "parallel" and "cross" manners. Finally, to reduce the computational complexity and improve the robustness to image rotation, the LPCP is extended to the uniform local parallel cross pattern (ULPCP) and the rotation-invariant local parallel cross pattern (RILPCP), respectively. Extensive experiments are performed on eight benchmark datasets. The experimental results validate the effectiveness, efficiency, robustness, and computational complexity of the proposed descriptors against eight state-of-the-art color texture descriptors to produce an in-depth comparison. Additionally, compared with a series of Convolutional Neural Network (CNN)-based models, the proposed descriptors still achieve competitive results.
RESUMO
Today, eye trackers are extensively used in studying human cognition. However, it is hard to analyze and interpret eye movement data from the cognitive comprehension perspective of poster reading. To find quantitative links between eye movements and cognitive comprehension, we tracked observers' eye movement for reading scientific poster publications. We model in this paper eye tracking fixation sequences between content-dependent Areas of Interests (AOIs) as a Markov chain. Furthermore, we use the fact that a Markov chain is a special case of information or communication channel. Then, the gaze transition can be modeled as a discrete information channel, the gaze information channel. Next, some traditional eye tracking metrics, together with the gaze entropy and mutual information of the gaze information channel are calculated to quantify cognitive comprehension for every participant. The analysis of the results demonstrate that the gaze entropy and mutual information from individual gaze information channel are related to participants' individual differences. This is the first study that eye tracking technology has been used to assess the cognitive comprehension of poster reading. The present work provides insights into human cognitive comprehension by using the novel gaze information channel methodology.
RESUMO
Currently, visual sensors are becoming increasingly affordable and fashionable, acceleratingly the increasing number of image data. Image retrieval has attracted increasing interest due to space exploration, industrial, and biomedical applications. Nevertheless, designing effective feature representation is acknowledged as a hard yet fundamental issue. This paper presents a fusion feature representation called a hybrid histogram descriptor (HHD) for image retrieval. The proposed descriptor comprises two histograms jointly: a perceptually uniform histogram which is extracted by exploiting the color and edge orientation information in perceptually uniform regions; and a motif co-occurrence histogram which is acquired by calculating the probability of a pair of motif patterns. To evaluate the performance, we benchmarked the proposed descriptor on RSSCN7, AID, Outex-00013, Outex-00014 and ETHZ-53 datasets. Experimental results suggest that the proposed descriptor is more effective and robust than ten recent fusion-based descriptors under the content-based image retrieval framework. The computational complexity was also analyzed to give an in-depth evaluation. Furthermore, compared with the state-of-the-art convolutional neural network (CNN)-based descriptors, the proposed descriptor also achieves comparable performance, but does not require any training process.
