Keisuke Kameyama

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Keywords: Thinning algorithm Robustness against noise Scale space filtering Sketch image preprocessing a b s t r a c t We apply scale space filtering to thinning of binary sketch images by introducing a framework for making thinning algorithms robust against noise. Our framework derives multiple representations of an input image within multiple scales of(More)
We introduce a statistical shape descriptor for Sketch-Based Image Retrieval. The proposed descriptor combines feature information in near and far support regions defined for each sketch point. Two feature values are extracted from each point, corresponding to near and far support regions from the point's perspective, and used to populate a 2-D histogram(More)
Similarity of images in content-based image retrieval (CBIR) is a subjective measure varying by the user, and requires tuning according to the user's preference. Another issue in CBIR is the need of partial image matching. Structural modeling of the images can be promising in finding a small query image within a large database image. In this work, a(More)
Two methods for retrieval relevance optimization using the user’s feedback is proposed for a content-based image retrieval (CBIR) system. First, the feature space used in database image clustering for coarse classification is transferred to a preference feature space according to the user’s feedback by a map generated by supervised training, thereby(More)
In this paper, we propose an automatic retrieval method that addresses the problem of finding similar trademark images from a database when compared with an input. Our method is based on evaluating the compatibility of relative relations among extracted image contour segments between the input and the registered images using relaxation matching. The overall(More)
In this paper, we propose a relaxation-labeling algorithm for real-time contour-based image similarity retrieval that treats the matching between two images as a consistent labeling problem. To satisfy real-time response, our algorithm works by reducing the size of the labeling problem, thus decreasing the processing required. This is accomplished by adding(More)
Image relevance evaluation in conventional Content-Based Image Retrieval (CBIR) researches typically relied on a given criterion. However, it is important that this criterion can be changed according to the use of the image database. This work proposes a framework for tuning the parameters embedded in the relevance evaluation algorithm of a CBIR system, by(More)
We introduce an adaptation framework based on scale space filtering for making thinning algorithms robust against noise in sketch images. The framework takes a sketch image as input, produces a set of Gaussian blurred images of the input sketch and uses a thinning algorithm to produce thinned versions of the blurred images. The algorithm’s output is then(More)