Comparison of Various Improved-Partition Fuzzy c-Means Clustering Algorithms in Fast Color Reduction

This paper provides a comparative study of sev- eral enhanced versions of the fuzzy c -means clustering al- gorithm in an application of histogram-based image color reduction. A common preprocessing is performed before clus- tering, consisting of a preliminary color quantization, histogram extraction and selection of frequently occurring colors of the image. These selected colors will be clustered by tested c -means algorithms. Clustering is followed by another common step, which creates the output image. Besides conventional hard (HCM) and fuzzy c -means (FCM) clustering, the so-called generalized improved partition FCM algorithm, and several versions of the suppressed FCM (s-FCM) in its conventional and generalized form, are included in this study. Accuracy is measured as the average color difference between pixels of the input and output image, while efficiency is mostly characterized by the total runtime of the performed color reduction. Nu- merical evaluation found all enhanced FCM algorithms more accurate, and four out of seven enhanced algorithms faster than FCM. All tested algorithms can create reduced color images of acceptable quality

Local Adaptive Receptive Field Self-Organizing Map for Image Segmentation

A new self-organizing map with variable topology is introduced for image segmentation. The proposed network, called Local Adaptive Receptive Field Self-Organizing Map (LARFSOM-RBF), is a two-stage network capable of both color and border segment images. The color segmentation stage is responsibility of LARFSOM which is characterized by adaptive number of nodes, fast convergence and variable topology. For border segmentation RBF nodes are included to determine the border pixels using previously learned information of LARFSOM. LARFSOM-RBF was tested to segment images with different degrees of complexity showing promising results

Influence de la réduction des couleurs sur la détection des changements de plan dans les films d'animation

Dans ce papier nous proposons une technique de détection des changements de plan appliquée aux films d'animation. Cette technique est basée sur la mesure de distance entre histogrammes couleur d'images voisines. L'approche envisagée demande la définition d'une palette couleur réduite sur laquelle sont calculés les histogrammes. La détection des changements est alors effectuée par seuillage, le seuil étant déterminé automatiquement. Nous présentons des résultats obtenus avec quelques films sur lesquels une segmentation temporelle a été effectuée manuellement pour disposer d'une vérité terrain. Ces tests permettent d'évaluer l'influence du choix de la palette couleur choisie

Local Adaptive Receptive Field Self-Organizing Map for Image Segmentation

A new self-organizing map with variable topology is introduced for image segmentation. The proposed network, called Local Adaptive Receptive Field Self-Organizing Map (LARFSOM-RBF), is a two-stage network capable of both color and border segment images. The color segmentation stage is responsibility of LARFSOM which is characterized by adaptive number of nodes, fast convergence and variable topology. For border segmentation RBF nodes are included to determine the border pixels using previously learned information of LARFSOM. LARFSOM-RBF was tested to segment images with different degrees of complexity showing promising results