Video boundary detection is a task to divide a video into several segments based on event changes such as scenes or actions. The most common evaluation is to judge whether the distance between predicted boundaries and the ground truth boundaries is lower than allowed margin and then compute F1 score. However, we found that the evaluation only by F1 measure can lead to wrong conclusions since even completely random model can achieve inflated F1 when the number of predictions is large. To design a robust metric against chance, we propose Margin Matthews Correlation Coefficient (MMCC) as an extension of Matthews Correlation Coefficient (MCC) to video boundary detection with allowed margin. Although MCC is a robust metric against chance, it is not obvious that the same is true in video boundary detection due to allowed margin. Specifically, some definitions of MCC do not keep a constant as for the number of predicted boundaries. Therefore, we design MMCC so that the expected MMCC for random guessing will be zero, based on mathematical analysis. We empirically examine if MMCC is robust against completely random guessing and oversegmentation/undersegmentation, while F1 is not.

We consider the problem of 2D motion retargeting, which is to transfer the motion of one 2D skeleton to another skeleton of a different body shape. Existing methods decompose the input motion skeleton into dynamic (motion) and static (body shape, viewpoint angle, and emotion) features and synthesize a new skeleton by mixing up the features extracted from the different data. However, the resulting motion skeletons do not reflect subject-dependent factors that can stylize motion, such as skill and expressions, leading to unattractive results. In this work, we propose a novel network to separate subject-dependent and -independent motion features and to reconstruct a new skeleton with or without subject-dependent motion features. The core of our approach is adversarial feature disentanglement. The motion features and a subject classifier are trained simultaneously such that subject-dependent motion features do allow for between-subject discrimination, whereas subject-independent features cannot. The presence or absence of individuality is readily controlled by a simple summation of the motion features. Our method shows superior performance to the state-of-the-art method in terms of reconstruction error and can generate new skeletons while maintaining individuality.

In this paper, we propose a method that can handle censored data, data collected under the condition that the exact value is recorded only when the value is within a certain range, abbreviated information is recorded otherwise. It is known that existing methods that use mixture models with censored data suffer from (i) the existence of local optimum solutions and (ii) the need to compute the statistics of truncated distributions for parameter estimation. Our proposal, exemplar based censored mixture model (EBCM), overcomes these two difficulties at once by adopting the exemplar based model approach. The effectiveness of EBCM is confirmed by experiments on synthetic and real world dat sets.

In this paper, we propose a probabilistic model for analyzing the generalized interval valued matrix, a matrix that has scalar valued elements and bounded/unbounded interval valued elements. We derive a majorization minimization algorithm for parameter estimation and prove that the objective function is monotonically decreasing by the parameter update. An experiment shows that the proposed model well handles interval- valued elements and offers improved performance.

In this paper, we tackle the problem of extracting latent structure and patterns from multiple datasets that consist of users' rating scores and activity logs (click, view, visit, ...) in order to understand the typical users' behavior. Our proposed method is based on non-negative matrix factorization, and factorizes multiple matrices simultaneously while adopting Euclidean distance and generalized KL divergence for the rating matrix and the activity matrix, respectively. We derive an optimization algorithm that offers a theoretical guarantee that it can find a locally optimal solution. Our experiments show that the proposed method outperformed existing methods when measured by mean squared error, which implies that it can extract latent structure and patterns more precisely. We also confirm that the segmentation result by the proposal helps to analyze users' behavior.

In this paper, we tackle with the problem of analyzing datasets with different resolution such as a pair of user's individual data and user group's data, for example "userA visited shopA 5 times" and "users whose attributes are men purchased itemA 80 times in total". In order to establish a basic approach to this problem, we focus on the simplified scenario and propose a new probabilistic model called probabilistic non-negative inconsistent-resolution matrices factorization (pNimf). pNimf is rigorously derived from the data generative process using latent high-resolution data which underlie low-resolution data. We conduct experiments on real purchase log data and confirm that the proposed model provides superior performance, and that the performance improves as the number of low-resolution data increases. These results imply that our way of modeling using latent high-resolution data can become the basic approach to the problem of analyzing dataset with different resolution.

Recently, analysis of massive purchase histories is thought to be effective for marketing science. For successful sales strategies and advertisements, understanding of consumer's attitude (motivation toward purchasing) is important. However, marketers could obtain merely static attitude for the limited consumers using subjective questionnaires. We focus on purchase rhythm (regularity) as one approach to automatically extracting dynamic attitude transition. We propose "regular behavior measure" which quantifies how well each purchase corresponds to the user's own daily habits. It considers non-periodic habits as well as periodic habits using multiple probabilistic models, we consider purchase probability and the number of purchased dates simultaneously. We also define "influence weight" to quantify whether each item is purchased on a fixed day of the week (DoW) or year-month (YM) using Normalized Mutual Information. We analyze actual purchase data to show the effectiveness of our visualization that considers consumer's regularity transitions.

The k-means and fuzzy c-means algorithm are widely used for discovering clusters in data. In this paper, we propose a new acceleration method using blocked time step algorithm. It reduces the number of distance calculations efficiently by changing the frequency of updating membership grades (searching the nearest cluster centers). By restricted time step with the discrete number as a power of two, distance for the objective function will be calculated only synchronized objects at the blocked time. As a results, the experiments show the proposed algorithm reduces distance calculations by 20 -- 60% while keeping the clustering accuracy.

Microblogging services generate huge histories that suit animated visualization based on graph drawing, but response speeds are insufficient. This paper proposes an effective updating method for the real-time visualization of time varying data. We propose an initial positioning method by combining Additional Edge Resizing (AER) with Sorted Sequential Barycenter Merging (SSBM). AER resizes edges between existing nodes when new edges are added before updating the visualization. SSBM initially positions multiple new nodes sequentially through priority based on the degree of connections to the existing graph. The proposed method prevents a decrease in readability at data update and achieves fast convergence with high accuracy. Quantitative and qualitative evaluations using the energy function and RMSE are detailed along with some visualization results. A graph drawing tool based on HTML5 is also introduced as an implementation of the proposed method and present some use cases for twitter data.

This paper proposes a new method for displaying large-scale tag clouds. We use a topographical image that helps users to grasp the relationship among tags intuitively as a background to the tag clouds. We apply this interface to a blog navigation system and show that the proposed method enables users to find the desired tags easily even if the tag clouds are very large, 5,000 and above tags. Our approach is also effective for understanding the overall structure of a large amount of tagged documents.

Our new research project called concentrates on the creation of new lifestyles through research on communication science and intelligence integration. It is premised on the creation of such virtual communication partners as fairies and goblins that can be constantly at our side. We call these virtual communication partners mushrooms.To show the essence of ambient intelligence, we developed two multimodal prototype systems: mushrooms that watch, listen, and answer questions and a Quizmaster Mushroom. These two systems work in real time using speech, sound, dialogue, and vision technologies.We performed preliminary experiments with the Quizmaster Mushroom. The results showed that the system can transmit knowledge to users while they are playing the quizzes.Furthermore, through the two mushrooms, we found policies for design effects in multimodal interface and integration.