Segmentation of color images by chromaticity features using self-organizing maps

Abstract

Usually, the segmentation of color images is performed using cluster-based methods and the RGB space to represent the colors. The drawback with these methods is the a priori knowledge of the number of groups, or colors, in the image; besides, the RGB space is sensitive to the intensity of the colors. Humans can identify different sections within a scene by the chromaticity of its colors of, as this is the feature humans employ to tell them apart. In this paper, we propose to emulate the human perception of color by training a self-organizing map (SOM) with samples of chromaticity of different colors. The image to process is mapped to the HSV space because in this space the chromaticity is decoupled from the intensity, while in the RGB space this is not possible. Our proposal does not require knowing a priori the number of colors within a scene, and non-uniform illumination does not significantly affect the image segmentation. We present experimental results using some images from the Berkeley segmentation database by employing SOMs with different sizes, which are segmented successfully using only chromaticity features.Keywords: Segmentation of color images, color spaces, competitive neural networks. RESUMENUsualmente, la segmentación de imágenes de color se realiza empleando métodos de agrupamiento y el espacio RGB para representar los colores. El problema con los métodos de agrupamiento es que se requiere conocer previamente la cantidad de grupos, o colores, en la imagen; además de que el espacio RGB es sensible a la intensidad de colores. Los humanos podemos identificar diferentes secciones de una escena solo por la cromaticidad de los colores, ya que representa la característica que nos permite diferenciarlos entre sí. En este artículo se propone emular la percepción humana del color al entrenar un mapa auto-organizado (MAO) con muestras de cromaticidad de diferentes colores. La imagen a procesar es transformada al espacio HSV porque en tal espacio la cromaticidad es separada de la intensidad, mientras que en el espacio RGB no es posible. Nuestra propuesta no requiere conocer previamente la cantidad de colores que hay en una escena, y la iluminación no uniforme no afecta significativamente la segmentación de la imagen. Presentamos resultados experimentales utilizando algunas imágenes de la base de segmentación de Berkeley empleando MAOs de diferentes tamaños, las cuales son segmentadas exitosamente empleando únicamente características de cromaticidad.Palabras clave: Segmentación de imágenes de color, espacios de color, redes neuronales competitivas.

Segmentation of images by color features: A survey

“…As mentioned previously, the clustering techniques are popular and they are often employed, mainly the FCM because they are effective and easy to implement; but they require defining a priori the number of clusters in the data. Defining the number of clusters affects the number of parts obtained when the image is segmented; but, there are related works that address how computing the number of groups for FCM [44,68,82,83] . According to the reviewed works, the color image segmentation is precise when the FCM are used along with the L * a * b * and L * u * v * spaces to represent colors.…”

“…This problem is overcome by modelling the chromaticity as a two-element unit vector, where its orientation defines the chromaticity, as proposed in [39][40][41]43,44] . That is, let φ = [ h, s, v ] be a HSV color, the chromaticity is modeled as ψ = [ cosh, sinh ] , thus, the problem mentioned before is solved.…”

“…The genetically guided clustering attempts to achieve both avoidance of local extrema and minimal sensitivity to initialization. Garcia-Lamont et al [44] presented an approach to compute automatically the number of clusters so as to segment the images using FCM. A competitive neural network and a self-organizing map are trained with chromaticity samples of different colors; the neural networks process each pixel of the image to segment, where the activation occurrences of each neuron are collected in a histogram.…”

“…Neural networks (NN) are a computational approach, based on a large collection of neural units also known as artificial neurons, to freely model the way a biological brain solves problems with large groups of neurons connected by axons [61,62] . Neural networks offer important advantages in machine learning tasks [32][33][34]44,[63][64][65]196,197] , its high degree of parallelism allows very fast computational times and makes them suitable for real time applications, and good robustness to disturbances. Another important advantage in the case of image segmentation is that, neural networks permit accounting for spatial information.…”

“…That is, a NN trained with the colors of a given image cannot always recognize all the colors of a different image; hence, the NN must be trained with the colors of the new image. To overcome this drawback, in references [44] and [43] SOMs and a three-layered SOM were trained, respectively, with chromaticity samples of different colors represented in the HSV space. The NNs are trained just once and they can segment a given image without training them again.…”