Feature extraction for document image segmentation by pLSA model

In this paper, we propose a method for document image segmentation based on pLSA (probabilistic latent semantic analysis) model. The pLSA model is originally developed for topic discovery in text analysis using "bag-of-words" document representation. The model is useful for image analysis by "bag-of-visual words" image representation. The performance of the method depends on the visual vocabulary generated by feature extraction from the document image. We compare several feature extraction and description methods, and examine the relations to segmentation performance. Through the experiments, we show accurate content-based document segmentation is made possible by using pLSA-based method.ArticleThe Eighth IAPR Workshop on Document Analysis Systemsconference pape

A Fast Rendering Method for a Scene with Participating Media of Anisotropic Scattering Property

Stony Brook,New York, USA, June22-24 2005This paper presents an efficient technique for global illumination rendering of a scene with participating media. The rendering handling participating media is performed by ray marching method, which requires sampling along each view direction. The step size of the ray marching must be taken short to generate a high quality image and thus leads to very long computational time. One possible method to improve the computational cost is to exploit importance sampling.In this paper, we propose a method to determine the step size based on the importance sampling technique. For efficient sampling, the probability density function which is ”close” to the radiance distribution is required. In our method, 3D space is divided into a set of uniform grids.The radiance distribution is approximated using the grid structure. To deal with the participating media which has anisotropic scattering property, we use spherical harmonics to represent directional dependence of radiance distribution.Using this grid-based representation, fast calculation of good approximation of desirable probability density is made possible. Using this probability, high quality image can be rendered with fewer numberArticleComputer Graphics International 2005 (CGI2005):227-233 2005conference pape

Character Type Classification via Probabilistic Topic Model

ArticleInternational Journal of Signal Processing, Image Processing and Pattern Recognition. 5(2): 123-140 (2012)journal articl

A Connection Between GRBF and MLP

Both multilayer perceptrons (MLP) and Generalized Radial Basis Functions (GRBF) have good approximation properties, theoretically and experimentally. Are they related? The main point of this paper is to show that for normalized inputs, multilayer perceptron networks are radial function networks (albeit with a non-standard radial function). This provides an interpretation of the weights w as centers t of the radial function network, and therefore as equivalent to templates. This insight may be useful for practical applications, including better initialization procedures for MLP. In the remainder of the paper, we discuss the relation between the radial functions that correspond to the sigmoid for normalized inputs and well-behaved radial basis functions, such as the Gaussian. In particular, we observe that the radial function associated with the sigmoid is an activation function that is good approximation to Gaussian basis functions for a range of values of the bias parameter. The implication is that a MLP network can always simulate a Gaussian GRBF network (with the same number of units but less parameters); the converse is true only for certain values of the bias parameter. Numerical experiments indicate that this constraint is not always satisfied in practice by MLP networks trained with backpropagation. Multiscale GRBF networks, on the other hand, can approximate MLP networks with a similar number of parameters

A User Interface Using a Spiral Representation for Image Retrieval on a Mobile Terminal

10th asia pacific conference on Computer human interaction (APCHI '12), August 28-31, 2012 in Matsue-city, Shimane, Japan.To efficiently visualize a set of images for a similar image search system on a mobile terminal which is equipped with a touch screen, we propose a method that ranked images are represented in a spiral manner and they can be dragged along the spiral for finding a target image. We also propose efficient search operations for the system. As a result of comparison experiments between our system and a traditional one, evaluations of our system in terms of 'intuitive', 'beauty', 'fun', and 'novelty' were higher than those of traditional one.ArticleProceedings of the 10th asia pacific conference on Computer human interaction. :625-626 (2012)conference pape

Three dimensional structure of low-density nuclear matter

We numerically explore the pasta structures and properties of low-density nuclear matter without any assumption on the geometry. We observe conventional pasta structures, while a mixture of the pasta structures appears as a metastable state at some transient densities. We also discuss the lattice structure of droplets.Comment: 6 pages, 8 figure

Image categorization by a classifier based on probabilistic topic model

With rapid increase of number of accessible images and videos, ability to recognize visual information is getting more and more important for content-based information retrieval. Recently, probabilistic topic models, which were originally developed for text analysis, have been used for image categorization successfully. Usually, topics which represent contents of an image is detected based on the underlying probabilistic model, then image categorization is carried out using topic distribution as the input feature. Typical method is to use k-nearest neighbor classifier based on L2-distance after topic discovery. In the method, topic distribution is just treated as a feature point. In this paper, we propose a categorization method based on more natural use of the topic distribution, which is derived by using pLSA model. Categorization is carried out by estimating conditional probability p(categoryjdata). We present two types of image categorization tasks, scene classification and document image segmentation, and show the proposed method performs very well. In addition, we also examine the performance of the proposed method under the situation where only the limited number of labeled examples are available. We show our method can perform quite well even in the circumstances