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A Hierarchical deep model for food classification from photographs
( Heekyung Yang ),( Sungyong Kang ),( Chanung Park ),( Jeongwook Lee ),( Kyungmin Yu ),( Kyungha Min ) 한국인터넷정보학회 2020 KSII Transactions on Internet and Information Syst Vol.14 No.4
Recognizing food from photographs presents many applications for machine learning, computer vision and dietetics, etc. Recent progress of deep learning techniques accelerates the recognition of food in a great scale. We build a hierarchical structure composed of deep CNN to recognize and classify food from photographs. We build a dataset for Korean food of 18 classes, which are further categorized in 4 major classes. Our hierarchical recognizer classifies foods into four major classes in the first step. Each food in the major classes is further classified into the exact class in the second step. We employ DenseNet structure for the baseline of our recognizer. The hierarchical structure provides higher accuracy and F1 score than those from the single-structured recognizer.
Texture-based Hatching for Color Image and Video
( Heekyung Yang ),( Kyungha Min ) 한국인터넷정보학회 2011 KSII Transactions on Internet and Information Syst Vol.5 No.4
We present a texture-based hatching technique for color images and video. Whereas existing approaches produce monochrome hatching effects in considering of triangular mesh models by applying strokes of uniform size, our scheme produces color hatching effects from photographs and video using strokes with a range of sizes. We use a Delaunay triangulation to create a mesh of triangles with sizes that reflect the structure of an input image. At each vertex of this triangulation, the flow of the image is analyzed and a hatching texture is then created with the same alignment, based on real pencil strokes. This texture is given a modified version of a color sampled from the image, and then it is used to fill all the triangles adjoining the vertex. The three hatching textures that accumulate in each triangle are averaged and the result of this process across all the triangles forms the output image. We can also add a paper texture effect and enhance feature lines in the image. Our algorithm can also be applied to video. The results are visually pleasing hatching effects similar to those seen in color pencil drawings and oil paintings.
A Deep Approach for Classifying Artistic Media from Artworks
( Heekyung Yang ),( Kyungha Min ) 한국인터넷정보학회 2019 KSII Transactions on Internet and Information Syst Vol.13 No.5
We present a deep CNN-based approach for classifying artistic media from artwork images. We aim to classify most frequently used artistic media including oilpaint brush, watercolor brush, pencil and pastel, etc. For this purpose, we extend VGGNet, one of the most widely used CNN structure, by substituting its last layer with a fully convolutional layer, which reveals class activation map (CAM), the region of classification. We build two artwork image datasets: YMSet that collects more than 4K artwork images for four most frequently used artistic media from various internet websites and WikiSet that collects almost 9K artwork images for ten most frequently used media from WikiArt. We execute a human baseline experiment to compare the classification performance. Through our experiments, we conclude that our classifier is superior in classifying artistic media to human.
Feature-guided Convolution for Pencil Rendering
( Heekyung Yang ),( Kyungha Min ) 한국인터넷정보학회 2011 KSII Transactions on Internet and Information Syst Vol.5 No.7
We re-render a photographic image as a simulated pencil drawing using two independent line integral convolution (LIC) algorithms that express tone and feature lines. The LIC for tone is then applied in the same direction across the image, while the LIC for features is applied in pixels close to each feature line in the direction of that line. Features are extracted using the coherent line scheme. Changing the direction and range of the LICs allows a wide range of pencil drawing style to be mimicked. We tested our algorithm on diverse images and obtained encouraging results.
A Multi-Layered Framework for color pastel painting
( Heekyung Yang ),( Kyungha Min ) 한국인터넷정보학회 2017 KSII Transactions on Internet and Information Syst Vol.11 No.6
We present a computerized framework for producing color pastel painting from the visual information extracted from a photograph. To express color pastel painting, we propose a multi-layered framework where each layer possesses pastel stroke patterns of different colors. The stroke patterns in the separate layers are merged by a rendering equation based on a participating media rendering scheme. To produce the stroke patterns in each layer, we review the physical properties of pastels and the mechanism of a convolution framework, which is the most widely used scheme to simulate stick-shaped media such as pencils. We devise the following computational models to extend the convolution framework to produce pastel strokes: a bold noise model, which mimics heavy and clustered deposition of pigment, and a thick convolution filter model, which produces various pastel stroke patterns. We also design a stochastic color coordination scheme to mimic pastel artists` color expression and to separate strokes in different layers. To demonstrate the soundness of approach, we conduct several experiments using the models and compare the results with existing works or real pastel paintings. We present the results for several pastel paintings to demonstrate the excellent performance of our framework.
Simulation of Color Pencil Drawing using LIC
( Heekyung Yang ),( Kyungha Min ) 한국인터넷정보학회 2012 KSII Transactions on Internet and Information Syst Vol.6 No.12
We present a novel approach for the simulation of color pencil effects using line integral convolution (LIC) to produce pencil drawings from images. Our key idea is to use a bilateral convolution filter to simulate the various effects of pencil strokes. Our filter resolves the drawbacks of the existing convolution-based schemes, and presents an intuitive control to mimic the properties of pencil strokes. We also present a scheme that determines stroke directions from the shapes to be drawn. Smooth tangent flows are used for the pixels close to feature lines, and partially parallel flows inside regions. The background is rendered using a flow of fixed direction. Using different styles of stroke directions increases the realism of the resulting images. This approach produces convincing pencil drawing effects from photographs.