Machine Learning Based Color Classification by Means of Visually Evoked Potentials

Visually evoked potentials (VEPs) are widely used for diagnoses of different neurological diseases. Interestingly, there is limited research about the impact of the stimulus color onto the evoked response. Therefore, in our study we investigated the possibility of automatically classifying the stimulus color. The visual stimuli were selected to be red/black and green/black checkerboard patterns with equal light density. Both of these stimuli were presented in a random manner to nine subjects, while the electroencephalogram was recorded at the occipital lobe. After pre-processing and aligning the evoked potentials, an artificial neural network with one hidden layer was used to investigate the general possibility to automatically classify the stimulus color in three different settings. First, color classification with individually trained models, color classification with a common model, and color classification for each individual volunteer with a model trained on the data of the remaining subjects. With an average accuracy (ACC) of 0.83, the best results were achieved for the individually trained model. Also, the second (mean ACC = 0.76) and third experiments (mean ACC = 0.71) indicated a reasonable predictive accuracy across all subjects. Consequently, machine learning tools are able to appropriately classify stimuli colors based on VEPs. Although further studies are needed to improve the classification performance of our approach, this opens new fields of applications for VEPs.

A Fast Online Classification Method of Solid Wood Floors Based on Stochastic Sampling and Machine Learning

Solid wood floors are widely used as an interior decoration material, and the color of solid wood surfaces plays a decisive role in the final decoration effect. Therefore, the color classification of solid wood floors is the final and most important step before laying. However, research on floor classification usually focuses on recognizing complex and diverse features but ignores execution speed, which causes common methods to not meet the requirements of online classification in practical production. In this paper, a new online classification method of solid wood floors was proposed by combining probability theory and machine learning. Firstly, a probability-based feature extraction method (stochastic sampling feature extractor) was developed to obtain rapid key features regardless of the disturbance of wood grain. The stochastic features were determined by a genetic algorithm. Then, an extreme learning machine—as a fast classification neural network—was selected and trained with the selected stochastic features to classify solid wood floors. Several experiments were carried out to evaluate the performance of the proposed method, and the results showed that the proposed method achieved a classification accuracy of 97.78% and less than 1 ms for each solid wood floor. The proposed method has advantages including a high execution speed, great accuracy, and flexible adaptability. Overall, it is suitable for online industry production.

Preliminary Study on Sapphire Color Grading Method Based on Automatic Clustering Algorithm of Color Space Features

Traditionally, the color grading of sapphire is mainly based on the naked eye judgment of the appraiser. This judgment standard is not clear enough, and the judgment result has a greater subjective influence, which affects the accuracy of the classification. In this study, the GEM-3000 ultraviolet-visible spectrophotometer was selected, and the color features of 180 sapphire samples were extracted and classified using the CIE1976 color space of the device. The Kmeans algorithm was used to cluster analysis of 140 samples, and the separability of the color space features of different color levels was verified, and the center sample of each color level was obtained. The Euclidean distance between the centers of the remaining 40 samples is calculated, and each color grade prediction label is determined, and the sapphire color is automatically classified based on this. The experimental results show that the accuracy of sapphire color classification using the above method is 97.5%, which confirms the effect and accuracy of the artificial intelligence method in sapphire color classification.

Improving the quality of automated VIS–grading of Scots pine seeds using fuzzy logic algorithm

The automation of Scots pine seeds grading in the visible wavelength region – VIS-grading – is promising for conducting breeding and genetic experiments. This will reduce time costs and increase the accuracy of seed color classification compared to organoleptic techniques. When controlling VIS-grading, it is necessary to accurately detect and process the optical signal reflected from a single seed. The signal is based on the wavelength and amplitude of the optical beam. Earlier, using a spectrometer for Scots pine seeds from a natural stand of the Pavlovsky district of the Voronezh region, Russia, the boundaries of three spectrometric groups were established. In the real VIS-grading process, it is necessary to take into account the probabilistic deviations of random values of wavelengths and amplitudes. Therefore, on the basis of the Mamdani fuzzy logic theory developed an analyzing algorithm for controlling the VIS-grading quality. The algorithm consists of a sequence of logical terms that clearly define the specified VIS-grading seeds spectrometric parameters by a combination of wavelength and amplitude. The efficiency of Scots pine seeds VIS-grading using the algorithm is 98.9%.

The Semantic Structure of Color Terms in Arabic: A Cognitive Approach

The acquisition of the ability of perceiving and naming colors through language is an important topic in which languages vary and differ. The construction of color concepts and naming them are directly influenced by the culture and environment of each society. This can be noted by observing two aspects: Cognitive Semantics and its effect on the collective mind. This study focuses on the cognitive foundations of color terms in Arabic, and the semantic relation between the color concepts and terms in selected examples from both old and new usage of these color terms in Arabic. The study aims to cover the most dominant semantic components for color terms in the Arabic language, using the cognitive linguistic approach and the descriptive analytics method to determine the structure of cognitive perception of color terms in a language. Furthermore, the study stands on two pillars; the first reveals the way the conceptualization pattern of color terms occurs in Arab mindset displayed through selected examples of theoretical data on cognitive semantics, whereas the second addresses the semantic principle of color classification in Arabic. Finally, the conclusion, confirming the results about the notion that color naming in Arabic is based on the visual images associated with the colors in Arab environment, related to night and day. Hence, the color term becomes connected in the Arab mindset with the visual image, and under each color are colors similar to it in hue.

Color Classification and Texture Recognition System of Solid Wood Panels

Solid wood panels are widely used in the wood flooring and furniture industries, and paneling is an excellent material for indoor decoration. The classification of colors helps to improve the appearance of wood products assembled from multiple panels due to the differences in surface colors of solid wood panels. Traditional wood surface color classification mainly depends on workers’ visual observations, and manual color classification is prone to visual fatigue and quality instability. In order to reduce labor costs of sorting and to improve production efficiency, in this study, we introduced machine vision technology and an unsupervised learning technique. First-order color moments, second-order color moments, and color histogram peaks were selected to extract feature vectors and to realize data dimension reduction. The feature vector set was divided into different clusters by the K-means algorithm to achieve color classification and, thus, the solid wood panels with similar surface color were classified into one category. Furthermore, during twice clustering based on second-order color moment, texture recognition was realized on the basis of color classification. A sample of beech wood was selected as the research object, not only was color classification completed, but texture recognition was also realized. The experimental results verified the effectiveness of the technical proposal.