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Due to the prevalence of digital cameras, it is easy to retrieve digital images from the Internet. With the rapid development of digital image processing, databases, and Internet technologies, how to efficiently manage a large amount of digital images is very important. In this paper, we proposed a novel approach for automatic image annotation. We extract color, texture, and shape features from a...
Skin detection is used in applications ranging from face detection, tracking body parts and hand gesture analysis, to retrieval and blocking objectionable content. For robust skin segmentation and detection, we investigate color classification based on random forest. A random forest is a statistical framework with a very high generalization accuracy and quick training times. The random forest approach...
The work presented in this article consists in evaluating the contribution of the use of a texture-color combination in forest mapping. We transform a Spot image into different color spaces, then we extract nine parameters using Laws filter. The resulting set of parameters is reduced using MIFS algorithm based on mutual information. The results are promising and give satisfying classification results.
Image segmentation is very essential and critical to image processing and pattern recognition. It is known that, color image segmentation approaches are based on monochrome segmentation approaches operating in different color spaces. So in this paper, an improved method which uses the FSVM (fuzzy support vector machines) algorithm for color image segmentation in the HSI (hue-saturation-intensity)...
The support vector machine (SVM) provides a robust, accurate and effective technique for pattern recognition and classification. Although the SVM is essentially a binary classifier, it can be adopted to handle multi-class classification tasks. The conventional way to extent the SVM to multi-class scenarios is to decompose an m-class problem into a series of two-class problems, for which either the...
In this paper, we introduce a simple approach for detecting enteromorpha based on statistical learning of image features using support vector machines (SVM). The approach first classifies an enteromorpha image into two classes: enteromorpha and background. Then it extracts features from those two classes and uses them for training the SVM model. Finally, the predicting process is carried out in a...
In this paper, we propose a novel static hand gesture recognition method, which is based on a new support vector machine (abbreviated as SVM) classifier. SVM is a classification method based on statistics theory. Typical SVMs can be sufficient to deal with small scale data, but these methods cause a lot of computation in quadratic programming while dealing with non-linear problems. SVM combined with...
Semantic Image Annotation is a difficult task in Annotation Based Image Retrieval (ABIR) systems. Several techniques proposed in the past were lagging in efficiency and robustness. In this paper we are proposing a novel technique for automatically annotating multi-object images with higher accuracy. The colour entropy is used to eliminate the image background, and then we applied normalized cut principle...
We investigate to what extent combinations of features can improve classification performance on a large dataset of similar classes. To this end we introduce a 103 class flower dataset. We compute four different features for the flowers, each describing different aspects, namely the local shape/texture, the shape of the boundary, the overall spatial distribution of petals, and the colour. We combine...
This paper proposes an improved version of our previously introduced face detection system based on skin color segmentation and neural networks. The new system uses a support vector machine (SVM) based method for verification.
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