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Due to the advancement of computing and the power of the new hardware, more economical, it is now feasible to have thousands of images which can be analyzed to allow classification for its shape and/or color. Furthermore, techniques and efficiency of the classification depends on the characteristics to be obtained of images in order to compare and classify them according to their similarity. Some...
In order to describe the characteristics of medical image more fully in different scales and solve the problem of automatic image category annotation, multi-scale feature based medical image classification is discussed. A set of complementary image features in various scales, including gray-level, texture, shape features and features extracted in the frequency domain is used. An ensemble learning...
The subject of the paper is a method proposed for automated evaluation of the parameters of orthopantomograms of cystic disorders in human jawbones. The main problem in medical diagnostic is the low repeatability due to the subjective evaluation of images/pictures without using a tool for image processing. An available database of images of cysts is described in the paper. Results of a fast automated...
Computer-aided flower identification is a very useful tool for plant species identification aspect. In this paper, a study was made on a development of content based image retrieval system to characterize flower images efficiently. In this system, a method of structural pattern recognition based on probabilistic based recursive model is proposed to classify flower images. Experimental results show...
Image classification is a complex but important task especially in the areas of machine vision and image analysis such as remote sensing and face recognition. One of the challenges in image classification is finding an optimal set of features for a particular task because the choice of features has direct impact on the classification performance. However the goodness of a feature is highly problem...
This paper proposes a multiclass image retrieval method using combined color-frequency-orientation histogram. Shape information, obtained via edge detector and Hough Transform, is also incorporated into the new feature. The feature has shown advantage in both unsupervised and supervised learning on Corel image dataset containing 10 categories of 1000 complex scenes. In unsupervised learning, comparing...
Nowadays leaf image classification is very useful for both botanists and ordinary users since advanced imaging devices such as smart phones make it ever easier to capture leaf images for various tasks such as retrieval and classification. Most of existing approaches mainly utilize global shape features. In this paper, we propose to improve leaf image classification by taking both global features and...
Various types of orthogonal moments have been widely used for object recognition and classification. This paper presents an effective way of extracting texture features, Bessel Fourier moments, for image retrieval and classification applications. The Bessel Fourier moments are calculated for rotation invariance and perform better in terms of represent global features than orthogonal Fourier-Mellin...
In this paper, a semi-supervised technique based on support vector machine (SVM) for image classification and a Locality Sensitive Hashing (LSH) based searching algorithm to search for similarity of satellite imagery is presented. Given a query image, the goal is to retrieve matching images in the database based on the shape features extracted from satellite imagery data. The experimental results...
Medical images form an essential source of information for various important processes such as diagnosis of diseases, surgical planning, medical reference, research and training. Therefore, effective and meaningful search and classification of these images are vital. In this paper, the approaches of content-based image retrieval (CBIR) using low level features such as shape and texture are investigated...
We propose a new CGSF+PHOG descriptor and perform image classification using a novel EFM-KNN classifier, which combines the Enhanced Fisher Model (EFM) and the K Nearest Neighbor (KNN) decision rule. We integrate the oRGB-SIFT descriptor with other color SIFT features to produce the Color SIFT Fusion (CSF) and the Color Grayscale SIFT Fusion (CGSF) descriptors. The CGSF is integrated to the PHOG to...
Traditional content-based image classifications often fail to meet a user's need due to the `semantic gap' between the texture features and the semantic features of the image. Content-based indexing and retrieval of images requires a proper semantic description for image content. This paper presents a novel approach based on semantic features and Bayesian networks for image classification. A mapping...
In the agricultural market, uniform appearance quality of products is becoming more important. In this paper, we propose a total surface inspection and sorting system of oval-shaped agricultural product for fully automated ranking operation under uniformed criteria. The system consists of conveyance part, image acquisition part, and classification part. The conveyance part uses sensor-driven independent...
Scene classification from images is a challenging problem in computer vision due to its significant variability of scale, illumination, and view. Recently, Latent Dirichlet Allocation (LDA) model has grown popular in computer vision field, especially in scene labeling and classification. However, the effectiveness of the LDA model for the scene classification has not yet been addressed thoroughly...
Shape descriptors have been used frequently as features to characterize an image for classification and image retrieval tasks. The problem of recognizing classes of objects in images is important for annotation and indexing of Satellite image databases. In this paper, a comparison between shape and texture features for classification is presented. The classification is based on Support Vector Machine...
The objective is to develop a probabilistic approach for vision-based fire detection in videos. The proposed method analyzes the frame-to-frame changes of specific low-level features describing potential fire regions. These features are color, area size, surface coarseness, boundary roughness, and skewness within estimated fire regions. Because of flickering and random characteristics of fire, these...
In this paper we propose a robust and fully automated lumbar herniation diagnosis system based on clinical MRI data which will not only aid a radiologist to make a decision with increased confidence, but will also reduce the time needed to analyze each case. Our method is based on three steps : 1) We automatically label the five lumbar intervertebral discs in a sagittal MRI slice using a probabilistic...
We present a framework for interactive tracking of protein translocations between nuclei and cytoplasm of single cells. Initially, we segment selected keyframes using a novel interactive segmentation method, which employs a constrained density weighted Nyström method for eigenvector decomposition, and the geodesic commute distance for pixel classification. Tracking is achieved by both forward and...
This paper presents an algorithm to classify pixels in uterine cervix images into two classes, namely normal and abnormal tissues, and simultaneously select relevant features, using group sparsity. Because of the large variations in image appearance due to changes of illumination, specular reflections and other visual noise, the two classes have a strong overlap in feature space, whether features...
We propose a method for automatic emotion recognition as part of the FERA 2011 competition. The system extracts pyramid of histogram of gradients (PHOG) and local phase quantisation (LPQ) features for encoding the shape and appearance information. For selecting the key frames, K-means clustering is applied to the normalised shape vectors derived from constraint local model (CLM) based face tracking...
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