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This paper presents a novel double-stage classifier for handwritten chemical symbols recognition task. The first stage is rough classification, SVM method is used to distinguish non-ring structure (NRS) and organic ring structure (ORS) symbols, while HMM method is used for fine recognition at second stage. A point-sequence-reordering algorithm is proposed to improve the recognition accuracy of ORS...
The use of region shape descriptors was investigated for categorisation of textile design images. Images were segmented using MRF pixel labelling and the shapes of regions obtained were described with generic Fourier descriptors. Each image was represented as a bag of shapes. A simple yet competitive classification scheme based on nearest neighbour class-based matching was used. Classification performance...
In our daily life, it is much easier to distinguish which person is elder between two persons than how old a person is. When inferring a person's age, we may compare his or her face with many people whose ages are known, resulting in a series of comparative results, and then we conjecture the age based on the comparisons. This process involves numerous pairwise preferences information obtained by...
In this paper, we propose a novel multi-class graph boosting algorithm to recognize different visual objects. The proposed method treats subgraph as feature to construct base classifier, and utilizes popular error correcting output code scheme to solve multi-class problem. Both factors, base classifier and error-correcting coding matrix are considered simultaneously. And subgragphs, which are shareable...
Based on fuzzy C-means method and the characteristics of kernel-based method, the algorithm of kernel-based fuzzy clustering is presented, in which the objective function of fuzzy C-means is substituted by Gaussian kernel objective function. The approach of kernel-based fuzzy C-means clustering is used in the classification and recognition of remote sensing images, and the result shows that it can...
This paper presents an effective dimensionality reduction method based on support vector machine. By utilizing mapping vectors from support vector machine for dimensionality reduction purpose, we obtain features which are computationally efficient, providing high classification accuracy and robustness especially in noisy environment. These characteristics are acquired from the generalization capability...
We propose a bag-of-hierarchical-co-occurrence features method incorporating hierarchical structures for image classification. Local co-occurrences of visual words effectively characterize the spatial alignment of objects' components. The visual words are hierarchically constructed in the feature space, which helps us to extract higher-level words and to avoid quantization error in assigning the words...
Automatic recognition of printed mathematical symbols is a fundamental problem for recognition of mathematical expressions. Several classification techniques has been previously used, but there are very few works that compare different classification techniques on the same database and with the same experimental conditions. In this work we have tested classical and novelty classification techniques...
We consider the problem of classifying documents containing multiple unordered pages. For this purpose, we propose a novel bag-of-pages document representation. To represent a document, one assigns every page to a prototype in a codebook of pages. This leads to a histogram representation which can then be fed to any discriminative classifier. We also consider several refinements over this initial...
In recent years bag-of-visual-words representations have gained increasing popularity in the field of image classification. Their performance highly relies on creating a good visual vocabulary from a set of image features (e.g. SIFT). For real-world photo archives such as Flicker, codebooks with larger than a few thousand words are desirable, which is infeasible by the standard k-means clustering...
The ways distances are computed or measured enable us to have different representations of the same objects. In this paper we want to discuss possible ways of merging different sources of information given by differently measured dissimilarity representations. We compare here a simple averaging scheme [1] with dissimilarity forward selection and other techniques based on the learning of weights of...
Previous Multiple Kernel Learning approaches (MKL) employ different kernels by their linear combination. Though some improvements have been achieved over methods using single kernel, the advantages of employing multiple kernels for machine learning are far from being fully developed. In this paper, we propose to use “high order kernels” to enhance the learning of MKL when a set of original kernels...
Based on the framework of support vector machines (SVM) using one against one (OAO) strategy, a new kernel method based on the correlation coefficients of neighbor bands is proposed to raise the classification accuracy by combining the characteristics of hyperspectral image. This algorithm assigns weights to different bands in the kernel function according to the amount of useful information that...
This paper focuses on audio-visual (using facial expression, shoulder and audio cues) classification of spontaneous affect, utilising generative models for classification (i) in terms of Maximum Likelihood Classification with the assumption that the generative model structure in the classifier is correct, and (ii) Likelihood Space Classification with the assumption that the generative model structure...
Automatic facial expression recognition is a challenging problem in computer vision, and has gained significant importance in applications of human-computer interaction. This paper presents a new appearance-based feature descriptor, the Local Directional Pattern Variance (LDPv), to represent facial components for human expression recognition. In contrast with LDP, the proposed LDPv introduces the...
All Han-based scripts (Chinese, Japanese, and Korean) possess similar visual characteristics. Hence system development for identification of Chinese, Japanese and Korean scripts from a single document page is quite challenging. It is noted that a Han-based document page might also have Roman script in them. A multi-script OCR system dealing with Chinese, Japanese, Korean, and Roman scripts, demands...
This paper proposes an image classification method based on extracting image features using Haar random forests and combining them with a spatial matching kernel SVM. The method works by combining multiple efficient, yet powerful, learning algorithms at every stage of the recognition process. On the task of identifying aquatic stonefly larvae, the method has state-of-the-art or better performance,...
Bag-of-Words is widely used to describe images for image classification. However, this approach is limited because the spatial relation over visual words is not well exploited and also it is difficult to generate a single comprehensive vocabulary. In this paper, we propose novel effective schemes to handle these two issues. First, we propose a structure propagation technique to build more reasonable...
Classifying the heterogeneous classes present in the hyper spectral image is one of the recent research issues in the field of remote sensing. The classification accuracy can be improved if and only if both the feature extraction and classifier selection are proper. As the classes present in the hyper spectral image are having different textures, textural classification is entertained. Wavelet based...
We present a successful design for a high-performance, low-resource-consuming hardware for Support Vector Classification and Support Vector Regression. The system has been implemented on a low cost FPGA device and exploits the advantages of parallel processing to compute the feed forward phase in support vector machines. In this paper we show that the same hardware can be used for classification problems...
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