Composite Feature Vector Research Articles

Detection of Lungs Status Using Morphological Complexities of Respiratory Sounds

Traditionally, the clinical diagnosis of a respiratory disease is made from a careful clinical examination including chest auscultation. Objective analysis and automatic interpretation of the lung sound based on its physical characters are strongly warranted to assist clinical practice. In this paper, a new method is proposed to distinguish between the normal and the abnormal subjects using the morphological complexities of the lung sound signals. The morphological embedded complexities used in these experiments have been calculated in terms of texture information (lacunarity), irregularity index (sample entropy), third order moment (skewness), and fourth order moment (Kurtosis). These features are extracted from a mixed data set of 10 normal and 20 abnormal subjects and are analyzed using two different classifiers: extreme learning machine (ELM) and support vector machine (SVM) network. The results are obtained using 5-fold cross-validation. The performance of the proposed method is compared with a wavelet analysis based method. The developed algorithm gives a better accuracy of 92.86% and sensitivity of 86.30% and specificity of 86.90% for a composite feature vector of four morphological indices.

Recognition of Semantic Content in Image and Video

ABSTRACT This paper addresses the problem of recognizing semantic content from images and video for content based retrieval purposes. Semantic features are derived from a collection of low-level features based on color, texture and shape combined together to form composite feature vectors. Both Manhattan distance and Neural Networks are used as classifiers for recognition purposes. Discrimination is done using five semantic classes viz. mountains, forests, flowers, highways and buildings. The composite feature is represented by a 26-element vector comprising of 18 color components, 2 texture components and 6 shape components. General Terms Semantic content recognition Keywords Color, Texture, Shape. 1. INTRODUCTION Automated schemes for content based image retrieval (CBIR) are usually based on low-level features like color, texture and shape. Recognition of such low-level content has been extensively studied over the last couple of decades. However humans are more interested in image retrieval based on semantic or high-level content i.e. images or video frames need to be retrieved which are semantically similar to some given image, rather than visually. This is a more difficult proposition firstly because automated systems are not capable to directly recognizing semantic concepts, and secondly because semantic concepts are human abstractions and they have no fixed definitions e.g. a house or a car can have all types of colors, textures and shapes. For semantic content recognition therefore systems are first designed to retrieve a set of low-level features and then at a higher level a mapping is done to associate a set of low-level features with one or more high-level concept. This paper addresses the problem of recognizing high level concepts from images and video by utilizing a number of low-level features. The paper is organized as follows: section 2 provides an overview of related work, section 3 outlines the proposed methodology, section 4 provides details of the dataset and experimental results obtained, and section 5 provides the overall conclusions and the scope for future research.

Textile Image Retrieval Using Composite Feature Vectors of Color and Wavelet Transformed Textural Property

It is known that wavelet transform provides very useful feature values in analyzing various types of images. This paper presents a novel approach for content-based textile image retrieval which uses composite feature vectors of low-level color feature from spatial domain and second-order statistic features from wavelet-transformed sub-band coefficients. Even though color histogram itself is efficient and most used signature for CBIR, it is unable to carry local spatial information of pixel and generate inaccurate retrieval results especially in large image data set. In this paper, we extract texture features such as contrast, homogeneity, ASM(angular-second momentum) and entropy from decomposed sub-band images by wavelet transform and utilize these multiple feature vector to retrieve textile images combining with color histogram. From the experimental results it is proven that the proposed approach is efficiently retrieve the desired images from a large set of textile image database.

Feature fusion of mechanical faults based on evolutionary computation

Automatically and accurately detecting the type and severity of faults in a machine is very important to condition monitoring as well as quality inspection. In this paper, we propose an evolutionary algorithm for mechanical fault recognition based on genetic programming (GP) and information fusion techniques. The improved approach can generate composite feature vectors with more information about machine faults, which are used in automatic recognition of different mechanical faults without prior knowledge of the machine itself. In addition, normalisation and bootstrap are used as a pre-processing approach for primitive features. We also compare the recognition performance of different composite feature vectors and show that our proposed approach can perform the task of mechanical fault recognition more effectively.