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The classification accuracy of underwater image, which have the special image characteristic, is lower than the corresponding result of images in the air. A study was carried out to underwater image classification with deep convolutional neural networks and the classification ability was improved with two data augmentation methods. The experiments showed that the submarine image classification with...
Nowadays, deep learning is a technique that takes place in many computer vision related applications and studies. While it is put in the practice mostly on content based image retrieval, there is still room for improvement by employing it in diverse computer vision applications. In this study, we aimed to build a Convolutional Neural Network (CNN) based Facial Expression Recognition System (FER),...
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...
In this paper, we propose a novel multiple categories classification approach based on the Bayesian decision classification method using the dense SIFT feature. First, the dense scale invariant feature transform (SIFT) feature for each image belonging to various categories is extracted as image representation. Second, the statistical models for multiple categories are established by estimating the...
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...
Various statistical methods such as co-occurrence matrix, local binary patterns and spectral approaches such as Gabor filters have been used for generating global features for image classification. However, global image features fail to distinguish between local variations within an image. Bag-of-visual-words (BoVW) model do capture local variations in an image, but typically do not consider spatial...
This paper proposes a new Spatial Pyramid representation approach for image classification. Unlike the conventional Spatial Pyramid, the proposed method is invariant to rotation changes in the images. This method works by partitioning an image into concentric rectangles and organizing them into a pyramid. Each pyramidal region is then represented using a histogram of visual words. Our experimental...
Due to heavy clutters and occlusions of complex background, natural images contain complex features in data structure which often cause errors in image classification. In this paper, we propose semi-supervised bi-dictionary learning for image classification with smooth representation-based label propagation (SRLP) which extends reconstruction-based classification in a probabilistic manner. First,...
Batik is one of the cultural heritages in Indonesia. Batik has many types spread around Indonesia. Related to the diversity of batik, an effort to develop a database to preserve batik information is required. Searching batik information from the database by using keywords such as the province name where a batik came from, sometimes is difficult. In some cases, people only has a batik image without...
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...
This paper presents a novel relational database architecture aimed to visual objects classification and retrieval. The framework is based on the bag-of-features image representation model combined with the Support Vector Machine classification and is integrated in a Microsoft SQL Server database.
In this paper a suitable methodology for the improvement of the reliability of results in classification systems based on 3D images is proposed. More in detail, it is based on the knowledge of the uncertainty of the features constituting the 3D image (obtained processing a pair of two 2D stereoscopic images) and on a suitable statistical approach providing a confidence level to the classification...
With exploiting a small number of labeled images and a large number of unlabeled images, semi-supervised learning has attracted centralized attention in recent years. The representative works are Laplacian and Hessian regularization methods. However, Laplacian method tends to a constant value and poor generalization in the process of classification. Although Hessian energy can properly forecast the...
Classification of scenes along the semantic categories has received tremendous attention from researchers working in the field of computer vision. The content and the context information obtained from scenes at various levels of granularity have been used to solve the problem of classification of scenes. We propose a simple approach for classifying the scenes on the broader semantic lines of categories,...
This paper proposes a simple spatial feature combined with temporal characteristics to classify human interactions from surveillance cameras, which are far from the action scene. For the first stage, data is collected from a horizontal view. Then, the history of distance between two persons is stored during time as a temporal feature called distance signature. We use Spatio-Temporal Interest Points...
This paper presents a novel image classification scheme, named high order statistics based maximum a posterior (HOS-MAP). To bridge the gap between human judgment and machine intelligence, this framework first builds dissimilarity representations in a modified pseudo-Euclidean space. Then, the information of the dissimilarity increments distribution of each category is achieved based on high-order...
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