Kazuhiko Murasaki

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Abstract This paper studies clothing and attribute recognition in the fashion domain. Specifically, in this paper, we turn our attention to the compatibility of clothing items and attributes (Fig 1). For example, people do not wear a skirt and a dress at the same time, yet a jacket and a shirt are a preferred combination. We consider such inter-object or(More)
How can a machine learn to recognize visual attributes emerging out of online community without a definitive supervised dataset? This paper proposes an automatic approach to discover and analyze visual attributes from a noisy collection of image-text data on the Web. Our approach is based on the relationship between attributes and neural activations in the(More)
Estimating the nutritional value of food based on image recognition is important to health support services employing mobile devices. The estimation accuracy can be improved by recognizing regions of food objects and ingredients contained in those regions. In this paper, we propose a method that estimates nutritional information based on segmentation and(More)
This paper proposes a novel method of discovering a set of image <i>contents</i> sharing a specific <i>context</i> (attributes or implicit meaning) with the help of image collections obtained from social curation platforms. Socially curated contents are promising to analyze various kinds of multimedia information, since they are manually filtered and(More)
Many studies on action recognition from the third-person viewpoint have shown that articulated human pose can directly describe human motion and is invariant to view change. However, conventional algorithms that estimate articulated human pose cannot handle ego-centric images because they assume the whole figure appears in the image; only a few parts of the(More)
In this paper, we propose a robust and real-time 3D human shape reconstruction method in daily life spaces to make practical voxel-based motion capture systems. Our algorithm extracts human silhouette and reconstructs human shape via volume intersection from multi view point images. The method presented in this paper is based on energy minimization via(More)
This paper proposes a novel geometric verification method to handle 3D viewpoint changes under cluttered scenes for robust object recognition. Since previous voting-based verification approaches, which enable recognition in cluttered scenes, are based on 2D affine transformation, verification accuracy is significantly degraded when viewpoint changes occur(More)
We present a structured inference approach in deep neural networks for multiple attribute prediction. In attribute prediction, a common approach is to learn independent classifiers on top of a good feature representation. However, such classifiers assume conditional independence on features and do not explicitly consider the dependency between attributes in(More)
This paper proposes an articulated pose estimation method based on the pose prior for adaptation that is scene specific. In this research field, various approaches to estimate articulated human pose have been proposed and many researchers have tried to improve pose estimation accuracy for shared datasets. On the other hand, it is not common to use datasets(More)
In this paper, we propose a novel method to detect boundaries and estimate figure/ground assignments simultaneously. The proposed approach is based on the observation that the mid-level feature expression for boundary detection can represent local shape of boundaries with high accuracy and high speed [1]. We use figure/ground information to enhance the(More)