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Nowadays the CNN is widely used in practical applications for image classification task. However the design of the CNN model is very professional work and which is very difficult for ordinary users. Besides, even for experts of CNN, to select an optimal model for specific task may still need a lot of time (to train many different models). In order to solve this problem, we proposed an automated CNN...
Collaborative representation based classifier (CRC) and its probabilistic improvement ProCRC have achieved satisfactory performance in many image classification applications. They, however, do not comprehensively take account of the structure characteristics of the training samples. In this paper, we present an extended probabilistic collaborative representation based classifier (EProCRC) for image...
Sparse representation, which represents the test sample as a linear combination of the whole training samples, achieved great success in face recognition. It can obtain a good performance if there exist enough training samples. However, the number of face images of a subject is usually limited in real face recognition systems. In this paper, in order to obtain more representations of a face, we propose...
PCANet is a simple network using Principal Component Analysis (PCA) for image classification and obtained high accuracies on a variety of datasets. PCA projects explanatory variables on a subspace that the first component has the largest variance. On the other hand, Partial Least Squares (PLS) regression projects explanatory variables on a subspace that the first component has the largest covariance...
A label consistent recursive least squares dictionary learning algorithm, LC-RLSDLA, is proposed to learn discriminative dictionaries for image classification based on sparse coding. The class label information and a label consistency term are used in the cost function to enforce discriminability among the sparse codes. Two operation modes are derived for the LC-RLSDLA: the supervised learning mode,...
Plankton image classification plays an important role in the ocean ecosystems research. Recently, a large scale database for plankton classification with over 3 million images annotated with over 100 classes was released. However, the database suffers from imbalanced class distribution in which over 90% of images belong to only 5 classes. Due to this class-imbalance problem, the existing classification...
A new approach called Fuzzy Extended Feature Line (FEFL) is proposed for image classification in this paper, which retain the advantages and ideas of Nearest Feature Line (NFL). The proposed FEFL use NFL to extend the prototype image sample set. Fuzzy K-Nearest Neighbor is applied for adding the new suitable samples to the prototype sample set. Experimental results on ORL face database and finger-knuckle-print...
With the worldwide strengthening of anti-terrorism and other identity verification, the products based on face recognition are used in real life more and more. The recognition as an important ways has become the focus of academic research in the world. Face recognition accuracy can be improved by increasing the number of training samples, but increasing number will result in a large computing complexity...
In this work, a new training principle is introduced for unsupervised learning that makes the learned representations more efficient and useful. Using partially corrupted inputs instead, the denoising Auto encoder can obtain more robust and representative pattern of inputs than the traditional learning methods. Besides, this denoising Auto encoder can be stacked to form a deep network. The whole framework...
The paper proposes a new approach to a decision making process based on face recognition algorithms. The proposed approach is based on the use of the uncertainty on the information used in the classification process to provide suitable level of confidence to the system output. As a case study, it is applied to a specific face recognition classification algorithm, namely the one based on Linear Discrimination...
In this paper, we focus on the issue of building up a training set for the task of image classification at minimal labeling costs. It is a topic that has attracted the considerable attention in the recent years. We propose a novel active learning algorithm with optimal distribution. In order to solve the problems of the noisy distribution and the sampling bias in the actively sampling process, the...
This study proposes an automatic image processing procedure in order to facilitate regular updating of the land-use map of Puerto Rico, which is a key dataset for the Xplorah Planning Support Systems. The procedure is based on the contextual reclassification of digital high resolution aerial photographs that were preclassified using a decision tree classifier. For the contextual reclassification the...
This paper consists of development of detection strategies for face recognition tasks and to access its feasibility for forensic analysis using the FERET face database Author has used global feature extraction technique using statistical method for image classification. Facial images of three subjects with different expression and angles are used for classification. Principal Component Analysis has...
The capability to visually discern possible obstacles from the sky would be a valuable asset to a UAV for avoiding both other flying vehicles and static obstacles in its environment. The main contribution of this article is the presentation of a feasible approach to obstacle avoidance based on the segmentation of camera images into sky and non-sky regions. The approach is named the Sky Segmentation...
Face recognition is a challenging problem in computer vision and human computer interaction. Texture is the surface property which is used to identify and recognize objects in an image. Texture based facial recognition is a fast growing research area in recent years. The LBP method is based on characterizing the local image texture by local texture patterns. In this paper texture based face recognition...
Boosting is a versatile machine learning technique that has numerous applications including but not limited to image processing, computer vision, data mining etc. It is based on the premise that the classification performance of a set of weak learners can be boosted by some weighted combination of them. There have been a number of boosting methods proposed in the literature, such as the AdaBoost,...
In this paper, a novel Sparsely Encoded Local Descriptor (SELD) is proposed for face recognition. Compared with K-means or Random-projection tree based previous methods, sparsity constraint is introduced in our dictionary learning and sequent image encoding, which implies more stable and discriminative face representation. Sparse coding also leads to an image descriptor of summation of sparse coefficient...
This paper examines the effectiveness of geometric feature descriptors, common in computer vision, for false positive reduction and for classification of lung nodules in low dose CT (LDCT) scans. A data-driven lung nodule modeling approach creates templates for common nodule types, using active appearance models (AAM); which are then used to detect candidate nodules based on optimum similarity measured...
As texture information among pixels can be effectively represented using Local binary patterns (LBPs), image descriptors built using LBPs or its variants have been frequently used for various image analysis applications, e.g. medical image and texture image classification and retrieval. However, neither LBP nor any of its existing variants can be used to build descriptors for classifying multimodal...
Texture feature has been widely used in image segmentation, classification, retrieval and many others. Among various approaches to texture feature extraction, Gabor filtering has emerged as one of the most popular in recent years. Gabor filter-based texture feature extractor is in fact a Gabor filter bank defined by its parameters including frequencies, orientations and smoothing parameters of the...
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