Akisato Kimura

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This report proposes a new method for achieving precise video segmentation without any supervision or interation. The main contributions of this report include 1) the introduction of framewise segmentation based on the maximum a posteriori (MAP) estimation of the Markov random field (MRF) with graph cuts and saliency– driven priors, and 2) the updating of(More)
Canonical correlation analysis (CCA) is a powerful tool for analyzing multi-dimensional paired data. However, CCA tends to perform poorly when the number of paired samples is limited, which is often the case in practice. To cope with this problem, we propose a semi-supervised variant of CCA named SemiCCA that allows us to incorporate additional unpaired(More)
Change-point detection is the problem of finding abrupt changes in time-series, and it is attracting a lot of attention in the artificial intelligence and data mining communities. In this paper, we present a supervised learning based change-point detection approach in which we use the separability of past and future data at time t (they are labeled as +1(More)
Automatic video segmentation plays an important role in a wide range of computer vision and image processing applications. Recently, various methods have been proposed for this purpose. The problem is that most of these methods are far from real-time processing even for low-resolution videos due to the complex procedures. To this end, we propose a new and(More)
Social media has become ubiquitous. Tweets and other user-generated content have become so abundant that better tools for information organization are needed in order to fully exploit their potential richness. ”Social curation” has recently emerged as a promising new framework for organizing and adding value to social media, complementing the traditional(More)
Previously, we proposed a histogram-based quick signal search method called Time-Series Active Search (TAS). TAS is a method of searching through long audio or video recordings for a specified segment, based on signal similarity. TAS is fast; it can search through a 24-hour recording in 1 second after a query-independent preprocessing. However, an even(More)
This report proposes a new stochastic model of visual attention to predict the likelihood of where humans typically focus on a video scene. The proposed model is composed of a dynamic Bayesian network that simulates and combines a person’s visual saliency response and eye movement patterns to estimate the most probable regions of attention. Dynamic Markov(More)
Non-negative Matrix Factorization (NMF) is a traditional unsupervised machine learning technique for decomposing a matrix into a set of bases and coefficients under the non-negative constraint. NMF with sparse constraints is also known for extracting reasonable components from noisy data. However, NMF tends to give undesired results in the case of highly(More)
—Canonical correlation analysis (CCA) is a powerful tool for analyzing multi-dimensional paired data. However, CCA tends to perform poorly when the number of paired samples is limited, which is often the case in practice. To cope with this problem, we propose a semi-supervised variant of CCA named “semiCCA”that allows us to incorporate additional unpaired(More)
Recent studies in signal detection theory suggest that the human responses to the stimuli on a visual display are non-deterministic. People may attend to different locations on the same visual input at the same time. To predict the likelihood of where humans typically focus on a video scene, we propose a new stochastic model of visual attention by(More)