Multi-Scale Feature Fusion for Interior Style Detection

Text-based search engines can extract various types of information when a user enters an appropriate search query. However, a text-based search often fails in image retrieval when image understanding is needed. Deep learning (DL) is often used for image task problems, and various DL methods have successfully extracted visual features. However, as human perception differs for each individual, a dataset with an abundant number of images evaluated by human subjects is not available in many cases, although DL requires a considerable amount of data to estimate space ambiance, and the DL models that have been created are difficult to understand. In addition, it has been reported that texture is deeply related to space ambiance. Therefore, in this study, bag of visual words (BoVW) is used. By applying a hierarchical representation to BoVW, we propose a new interior style detection method using multi-scale features and boosting. The multi-scale features are created by combining global features from BoVW and local features that use object detection. Experiments on an image understanding task were conducted on a dataset consisting of room images with multiple styles. The results show that the proposed method improves the accuracy by 0.128 compared with the conventional method and by 0.021 compared with a residual network. Therefore, the proposed method can better detect interior style using multi-scale features

Furniture Style Compatibility Estimation by Multi-Branch Deep Siamese Network

As demands for understanding visual style among interior scenes increase, estimating style compatibility is becoming challenging. In particular, furniture styles are difficult to define due to their various elements, such as color and shape. As a result, furniture style is an ambiguous concept. To reduce ambiguity, Siamese networks have frequently been used to estimate style compatibility by adding various features that represent the style. However, it is still difficult to accurately represent a furniture’s style, even when using alternate features associated with the images. In this paper, we propose a new Siamese model that can learn from several furniture images simultaneously. Specifically, we propose a one-to-many ratio input method to maintain high performance even when inputs are ambiguous. We also propose a new metric for evaluating Siamese networks. The conventional metric, the area under the ROC curve (AUC), does not reveal the actual distance between styles. Therefore, the proposed metric quantitatively evaluates the distance between styles by using the distance between the embedding of each furniture image. Experiments show that the proposed model improved the AUC from 0.672 to 0.721 and outperformed the conventional Siamese model in terms of the proposed metric

Kataribe: a hosting service of historage repositories

MSR 2014 : the 11th Working Conference on Mining Software Repositories, May 31-June 01, 2014, Hyderabad, IndiaIn the research of Mining Software Repositories, code repository is one of the core source since it contains the product of software development. Code repository stores the versions of files, and makes it possible to browse the histories of files, such as modification dates, authors, messages, etc. Although such rich information of file histories is easily available, extracting the histories of methods, which are elements of source code files, is not easy from general code repositories. To tackle this difficulty, we have developed Historage, a fine-grained version control system. Historage repository is a Git repository which is built upon original Git repository. Therefore, similar mining techniques for general Git repositories are applicable to Historage repositories. Kataribe is a hosting service of Historage repositories, which enables researchers and developers to browse method histories on the web and clone Historage repositories to local. The Kataribe project aims to maintain and expand the datasets and features