Model Human Perception Research Articles

DarwinGSE: Towards better image retrieval systems for intellectual property datasets.

A trademark's image is usually the first type of indirect contact between a consumer and a product or a service. Companies rely on graphical trademarks as a symbol of quality and instant recognition, seeking to protect them from copyright infringements. A popular defense mechanism is graphical searching, where an image is compared to a large database to find potential conflicts with similar trademarks. Despite not being a new subject, image retrieval state-of-the-art lacks reliable solutions in the Industrial Property (IP) sector, where datasets are practically unrestricted in content, with abstract images for which modeling human perception is a challenging task. Existing Content-based Image Retrieval (CBIR) systems still present several problems, particularly in terms of efficiency and reliability. In this paper, we propose a new CBIR system that overcomes these major limitations. It follows a modular methodology, composed of a set of individual components tasked with the retrieval, maintenance and gradual optimization of trademark image searching, working on large-scale, unlabeled datasets. Its generalization capacity is achieved using multiple feature descriptions, weighted separately, and combined to represent a single similarity score. Images are evaluated for general features, edge maps, and regions of interest, using a method based on Watershedding K-Means segments. We propose an image recovery process that relies on a new similarity measure between all feature descriptions. New trademark images are added every day to ensure up-to-date results. The proposed system showcases a timely retrieval speed, with 95% of searches having a 10 second presentation speed and a mean average precision of 93.7%, supporting its applicability to real-word IP protection scenarios.

Using fuzzy measures for modeling human perception of uncertainty in artificial intelligence

We observe the importance of modeling human perceptions of various types of information for the construction of A. I. systems and highlight the need for modeling human perceptions of uncertainty. We introduce the concept of a fuzzy measure, μ, and discuss its use as a general structure for the representation of knowledge about an uncertain variable. We note the ability of the fuzzy measure to model various formulations of uncertain information in a unified format. We look at various properties of fuzzy, particularly the ability to fuse multiple fuzzy measures to form new measures. We introduce various operations on fuzzy measures motivated from probability theory such as the determination of expected values and variances. We emphasize the importance of the Choquet integral in these operations. We discuss two characterizing features of a fuzzy measure, its entropy and attitudinal character, and note their usefulness in helping select an appropriate measure. We finally look at the issue of answering questions about variables having information about its value modeled by a fuzzy measure. Here we must come to grips with the fact that for most measures a fundamental property of the probability measure, Prob(A) + Prob(A¯) = 1, does not hold.

Using Convolutional Neural Network Filters to Measure Left-Right Mirror Symmetry in Images

We propose a method for measuring symmetry in images by using filter responses from Convolutional Neural Networks (CNNs). The aim of the method is to model human perception of left/right symmetry as closely as possible. Using the Convolutional Neural Network (CNN) approach has two main advantages: First, CNN filter responses closely match the responses of neurons in the human visual system; they take information on color, edges and texture into account simultaneously. Second, we can measure higher-order symmetry, which relies not only on color, edges and texture, but also on the shapes and objects that are depicted in images. We validated our algorithm on a dataset of 300 music album covers, which were rated according to their symmetry by 20 human observers, and compared results with those from a previously proposed method. With our method, human perception of symmetry can be predicted with high accuracy. Moreover, we demonstrate that the inclusion of features from higher CNN layers, which encode more abstract image content, increases the performance further. In conclusion, we introduce a model of left/right symmetry that closely models human perception of symmetry in CD album covers.

Modeling cognitive and emotional processes: A novel neural network architecture

In our continuous attempts to model natural intelligence and emotions in machine learning, many research works emerge with different methods that are often driven by engineering concerns and have the common goal of modeling human perception in machines. This paper aims to go further in that direction by investigating the integration of emotion at the structural level of cognitive systems using the novel emotional DuoNeural Network (DuoNN). This network has hidden layer DuoNeurons, where each has two embedded neurons: a dorsal neuron and a ventral neuron for cognitive and emotional data processing, respectively. When input visual stimuli are presented to the DuoNN, the dorsal cognitive neurons process local features while the ventral emotional neurons process the entire pattern. We present the computational model and the learning algorithm of the DuoNN, the input information-cognitive and emotional-parallel streaming method, and a comparison between the DuoNN and a recently developed emotional neural network. Experimental results show that the DuoNN architecture, configuration, and the additional emotional information processing, yield higher recognition rates and faster learning and decision making.

A psychophysically plausible model for typicality ranking of natural scenes

Natural scenes constitute a very heterogeneous stimulus class. Each semantic category contains exemplars of varying typicality. It is, therefore, an interesting question whether humans can categorize natural scenes consistently into a relatively small number of categories, such as, coasts, rivers/lakes, forests, plains, and mountains. This is particularly important for applications, such as, image retrieval systems. Only if typicality is consistently perceived across different individuals, a general image-retrieval system makes sense. In this study, we use psychophysics and computational modeling to gain a deeper understanding of scene typicality. In the first psychophysical experiment, we used a forced-choice categorization task in which each of 250 natural scenes had to be classified into one of the following five categories: coasts, rivers/lakes, forests, plains, and mountains. In the second experiment, the typicality of each scene had to be rated on a 50-point scale for each of the five categories. The psychophysical results show high consistency between participants not only in the categorization of natural scenes, but also in the typicality ratings. In order to model human perception, we then employ a computational approach that uses an intermediate semantic modeling step by extracting local semantic concepts, such as, rock, water, and sand. Based on the human typicality ratings, we learn a psychophysically plausible distance measure that leads to a high correlation between the computational and the human ranking of natural scenes. Interestingly, model comparisons without a semantic-modeling step correlated much less with human performance, suggesting that our model is psychophysically very plausible.

A fuzzy logic‐based algorithm for cosmic‐ray hit rejection from single images

AbstractAn algorithm for cosmic‐ray rejection from single images is presented. The algorithm is based on modeling human perception using fuzzy logic. The proposed algorithm is specifically designed to reject multiple‐pixel cosmic ray hits that are larger than some of the point spread functions of the true astronomical sources. Experiments show that the algorithm can accurately reject ∼97.5% of the cosmic rays hits, while mistakenly rejecting 0.02% of the true astronomical sources. The major advantage of the presented algorithm is its computational efficiency. (© 2005 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)