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The quality of the training data used in a supervised image classification can impact on the accuracy of the resulting thematic map obtained. Here the effects of mis-labeled training cases on the accuracy of classifications by discriminant analysis and a support vector machine were explored. The accuracy of both classifiers varied with the amount and nature of mis-labeled training cases. In particular,...
Augmenting spectral features with spatial features for hyperspectral image classification has recently gained significant attention, as classification accuracy can often be improved by extracting spatial features from neighboring pixels. However, the resulting high dimensional input data, which are often difficult and expensive to obtain, require large quantities of labeled data to train a robust...
In this paper we present a 2-tier higher order Conditional Random Field which is used for land cover classification. The Conditional Random Field is based on probabilistic messages being passed along a graph to compute efficiently the conditional probability for a land cover class. Conventionally the information is passed among direct spatial neighbors to improve classification accuracy. The inclusion...
Recently, the sparse coding based image representation has achieved state-of-the-art recognition results on many benchmarks. In this paper, we propose Multi-cue Normalized Non-Negative Sparse Encoder (MN3SE) which enforces both the non-negative constraint and the shift-invariant constraint on top of the traditional sparse coding criteria, and takes multi-cue to further boost the performance. The former...
Remote Sensing Image classification is one of the major research areas due to its wide spectrum of applications including natural terrain feature classification, land use monitoring, ground water exploration, environmental disaster assessment and urban planning etc. All these applications give a great success to terrain use but the only thing required is the proper classification of remotely sensed...
Through learning small and large-scale image features, we can capture the local and architectural structure of tumor tissue from histology images. This is done by learning a hierarchy of dictionaries using sparse coding, where each level captures progressively larger scale and more abstract properties. By optimizing the dictionaries further using class labels, discriminating properties of classes...
This paper presents a novel image classification method based on integration of EEG and visual features. In the proposed method, we obtain classification results by separately using EEG and visual features. Furthermore, we merge the above classification results based on a kernelized version of Supervised learning from multiple experts and obtain the final classification result. In order to generate...
The general phenomenon for Image Classification is based on the Feature extraction mechanism. In every domain of image analysis, the classification accuracy is dependent on how better the feature set is generated which helps the machine to learn and predict the unknown sample class label. In this paper, a novel feature extraction mechanism is proposed and named as Counting Label Occurrence Matrix...
Content based classification approach is becoming necessary to support the retrieval and indexing of images. This paper uses Color features of an image to form a feature vector on which data pre-processing is applied. These features are then used by machine learning classifiers to classify the images. Classification accuracy is evaluated in two color spaces and image sizes. Empirical results show...
Important task in image database is to organize images into appropriate category using different features of images. Image classification is studied for many years. There are various techniques proposed to increase the accuracy of classification. In this paper a novel data mining based approach is proposed for content based image classification. Feature extraction and classification algorithms are...
Obscene video detection is a core technology to prevent inappropriate access of children or teenagers to obscene video contents. There are many obscene video or image detection methods such as skin region analysis based methods, global histogram based methods. However, accuracy of these methods are not high enough to be deployed in the real-world environment. In this paper, we propose an obscene video...
In this paper, we present a distributed computing framework for image classification towards the current challenge of image big data due to enormous streaming image data sources, such as image sharing over online social network and massive video surveillance streams from ubiquitous cameras all over our daily life. The proposed framework consists of four modules aiming at feature extraction, dimension...
The aim of this paper is to develop an effective classification approach based on Random Forest (RF) algorithm. Three fruits; i.e., apples, Strawberry, and oranges were analysed and several features were extracted based on the fruits' shape, colour characteristics as well as Scale Invariant Feature Transform (SIFT). A preprocessing stages using image processing to prepare the fruit images dataset...
This paper presents a classification approach based on Random Forests (RF) and Linear Discriminant Analysis (LDA) algorithms for classifying the different types of plants. The proposed approach consists of three phases that are pre-processing, feature extraction, and classification phases. Since most types of plants have unique leaves, so the classification approach presented in this research depends...
The workload associated with the daily job of a clinical radiologist has been steadily increasing as the volume of the archived and the newly acquired images grows. Computer-aided diagnostic systems are becoming an indispensable tool in automating image analysis and providing preliminary diagnosis that can help guide radiologist's decisions. In this paper, we introduce a novel metric to evaluate the...
Abstract-Image annotation has been identified to be a suitable means by which the semantic gap which has made the accuracy of Content-based image retrieval unsatisfactory be eliminated. However existing methods of automatic annotation of images depends on supervised learning, which can be difficult to implement due to the need for manually annotated training samples which are not always readily available...
This paper motivated to design and develops an automatic model for multi-class breast tissue segmentation in breast mammogram images. Various breast tissues are categorized by a novel texture features such as PTPSA-[Piece-wise Triangular Prism Surface Area], intensity difference and regular-intensity in mammogram images. Using CRF-[Classical Random Forest] method segmentation and classification of...
An improved classification method based on KMeans using HSV color feature is introduced in this paper. It is implemented by extracting three color features (hue, saturation, value) for K-Means clustering. Compared with the traditional K-Means clustering, the experimental results turn out that our proposed method is better than K-Means in classification accuracy and performance.
A novel image recognition method based on the improved BDBN (Bilinear Deep Belief Network) model is presented, optimized with a MKL (Multiple Kernel Learning) strategy. All kernel functions in MKL are replaced by hierarchical feature representations, and the number of kernels is set to the number of layers of BDBN. The method is performed on the standard Caltech101 image dataset. The experiments show...
In this paper we are interested in the semi-supervised image classification in large datasets. The main originality of the proposed technique resides in the fuzzy quantification of the salient object in each image in order to guide the semi-supervised learning process during the classification. Indeed, we detect the salient object in each image using soft image abstraction, which allows the subsequent...
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