58797822
Discriminatory power of handwritten words for writer recognition
Analysis of allographs (characters) and allograph combinations (words) is the key for the identification/verification of a writer's handwriting. While allographs are usually part of words and the segmentation of a word into allographs is a subjective process, analysis of handwritten words is a natural option, complementary to allograph and document-level analysis. We consider four different types of features obtained using both segmentation-based and segmentation-free approaches: (i) GSC (gradient, structural and concavity) features that are extracted from the cells of a grid superimposed on the word image (ii) WMR (word model recognizer) features, extracted from the cells of superimposed grids on the segmented characters (iii) SC (shape curvature) features that describe characters by the distribution of curvature values on their contours and (iv) SCON (shape context) features that measure the similarity between character contour shapes. Their individual and accumulated performance is evaluated for the writer identification and verification tasks on over 75000 words images, written by more than 1000 writers. Experimental results show that handwritten words are very effective in discriminating handwriting and that both segmentation-free and segmentation-based approaches are valid.
15108545
Individuality of handwritten characters
Analysis of handwritten characters (allographs) plays animportant role in forensic document examination. However,so far there lacks a comprehensive and quantitative study onindividuality of handwritten characters. Based on a largenumber of handwritten characters extracted from handwritingsamples of 1000 individuals in US, the individuality ofhandwritten characters has been quantitatively measuredthrough identification and verification models. Our studyshows that in general alphabetic characters bear more individualitythan numerals and use of a certain number ofcharacters will significantly outperform the global featuresof handwriting samples in handwriting identification andverification. Moreover, the quantitative measurement ofdiscriminative powers of characters offers a general guidancefor selecting most-informative characters in examiningforensic documents.
6883417
Fast k-nearest neighbor classification using cluster-based trees
Most fast k-nearest neighbor (k-NN) algorithms exploit metric properties of distance measures for reducing computation cost and a few can work effectively on both metric and nonmetric measures. We propose a cluster-based tree algorithm to accelerate k-NN classification without any presuppositions about the metric form and properties of a dissimilarity measure. A mechanism of early decision making and minimal side-operations for choosing searching paths largely contribute to the efficiency of the algorithm. The algorithm is evaluated through extensive experiments over standard NIST and MNIST databases.
9623784
Multi-type Features Based Web Document Clustering
Clustering has been demonstrated as a feasible way to explore the contents of document collection and organize search engine results. For this task, many features of Web page, such as content, anchor text, URL, hyperlink etc, can be exploited and different results can be obtained. We expect to provide a unified and even better result for end users. Some work have studied how to use several types of features together to perform clustering. Most of them focus on ensemble method or combination of similarity. In this paper, we propose a novel algorithm: Multi-type Features based Reinforcement Clustering (MFRC). This algorithm does not use a unique combine score for all feature spaces, but uses the intermediate clustering result in one feature space as additional information to gradually enhance clustering in other spaces. Finally a consensus can be achieved by such mutual reinforcement. And the experimental results show that MFRC also provides some performance improvement.
5296157
A Novel Scalable Algorithm for Supervised Subspace Learning
Subspace learning approaches aim to discover important statistical distribution on lower dimensions for high dimensional data. Methods such as principal component analysis (PCA) do not make use of the class information, and linear discriminant analysis (LDA) could not be performed efficiently in a scalable way. In this paper, we propose a novel highly scalable supervised subspace learning algorithm called as supervised Kampong measure (SKM). It assigns data points as close as possible to their corresponding class mean, simultaneously assigns data points to be as far as possible from the other class means in the transformed lower dimensional subspace. Theoretical derivation shows that our algorithm is not limited by the number of classes or the singularity problem faced by LDA. Furthermore, our algorithm can be executed in an incremental manner in which learning is done in an online fashion as data streams are received. Experimental results on several datasets, including a very large text data set RCV1, show the outstanding performance of our proposed algorithm on classification problems as compared to PCA, LDA and a popular feature selection approach, information gain (IG).
1990595
IMMC: incremental maximum margin criterion
Subspace learning approaches have attracted much attention in academia recently. However, the classical batch algorithms no longer satisfy the applications on streaming data or large-scale data. To meet this desirability, Incremental Principal Component Analysis (IPCA) algorithm has been well established, but it is an unsupervised subspace learning approach and is not optimal for general classification tasks, such as face recognition and Web document categorization. In this paper, we propose an incremental supervised subspace learning algorithm, called Incremental Maximum Margin Criterion (IMMC), to infer an adaptive subspace by optimizing the Maximum Margin Criterion. We also present the proof for convergence of the proposed algorithm. Experimental results on both synthetic dataset and real world datasets show that IMMC converges to the similar subspace as that of batch approach.
5468007
A robust motion planner for assembly robots
An emergency-tolerant topological motion planning method for manipulators in an assembly workspace is presented. The topological motion planning method is discussed. The method first partitions the configuration space into several connected blocks and constructs a characteristic network (CN). It then searches for a path in CN. The partition of C-space in a topological method is accurate and complete, which is especially suitable for guiding an emergency-tolerant robot.<<ETX>>
2491447
Unsupervised image segmentation using local homogeneity analysis
In this paper, a novel method is presented for unsupervised image segmentation based on local homogeneity analysis. First, a criterion for homogeneity of a certain pattern is proposed. Applying the criterion to local windows in the original image results in the "H-image". The high and low values of the H-image correspond to possible region boundaries and region interiors respectively. Then, a region growing method is used to segment the image based on the H-image. Finally, visually similar regions are merged together to avoid over-segmentation. Experimental results on real images show the effectiveness and robustness of the method.