• MISULJARYO - National Museum of Korea Art Journal
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• v.96
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• pp.18-53
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• 2019

The paintings Gyeongpodae Pavilion and Chongseokjeong Pavilion were recently donated to the National Museum of Korea and unveiled to the public for the first time at the 2019 special exhibition "Through the Eyes of Joseon Painters: Real Scenery Landscapes of Korea." These two paintings carry significant implications for understanding Joseon art history. Because the fact that they were components of a folding screen produced after a sightseeing tour of the Gwandong regions in 1557 has led to a broadening of our understanding of sixteenth-century landscape painting. This paper explores the art historical meanings of Gyeongpodae Pavilion and Chongseokjeong Pavilion by examining the contents in the two paintings, dating them, analyzing their stylistic characteristics, and comparing them with other works. The production background of Gyeongpodae Pavilion and Chongseokjeong Pavilion can be found in the colophon of Chongseokjeong Pavilion. According to this writing, Sangsanilro, who is presumed to be Park Chung-gan (?-1601) in this paper, and Hong Yeon(?~?) went sightseeing around Geumgangsan Mountain (or Pungaksan Mountain) and the Gwandong region in the spring of 1557, wrote a travelogue, and after some time produced a folding screen depicting several famous scenic spots that they visited. Hong Yeon, whose courtesy name was Deokwon, passed the special civil examination in 1551 and has a record of being active until 1584. Park Chung-gan, whose pen name was Namae, reported the treason of Jeong Yeo-rip in 1589. In recognition of this meritorious deed, he was promoted to the position of Deputy Minister of the Ministry of Punishments, rewarded with the title of first-grade pyeongnan gongsin(meritorious subject who resolved difficulties), and raised to Lord of Sangsan. Based on the colophon to Chongseokjeong Pavilion, I suggest that the two paintings Gyeongpodae Pavilion and Chongseokjeong Pavilion were painted in the late sixteenth century, more specifically after 1557 when Park Chung-gan and Hong Yeon went on their sightseeing trip and after 1571 when Park, who wrote the colophon, was in his 50s or over. The painting style used in depicting the landscapes corresponds to that of the late sixteenth century. The colophon further states that Gyeongpodae Pavilion and Chongseokjeong Pavilion were two paintings of a folding screen. Chongseokjeong Pavilion with its colophon is thought to have been the final panel of this screen. The composition of Gyeongpodae Pavilion recalls the onesided three-layered composition often used in early Joseon landscape paintings in the style of An Gyeon. However, unlike such landscape paintings in the An Gyeon style, Gyeongpodae Pavilion positions and depicts the scenery in a realistic manner. Moreover, diverse perspectives, including a diagonal bird's-eye perspective and frontal perspective, are employed in Gyeongpodae Pavilion to effectively depict the relations among several natural features and the characteristics of the real scenery around Gyeongpodae Pavilion. The shapes of the mountains and the use of moss dots can be also found in Welcoming an Imperial Edict from China and Chinese Envoys at Uisungwan Lodge painted in 1557 and currently housed in the Kyujanggak Institute for Korean Studies at Seoul National University. Furthermore, the application of "cloud-head" texture strokes as well as the texture strokes with short lines and dots used in paintings in the An Gyeon style are transformed into a sense of realism. Compared to the composition of Gyeongpodae Pavilion, which recalls that of traditional Joseon early landscape painting, the composition of Chongseokjeong Pavilion is remarkably unconventional. Stone pillars lined up in layers with the tallest in the center form a triangle. A sense of space is created by dividing the painting into three planes(foreground, middle-ground, and background) and placing the stone pillars in the foreground, Saseonbong Peaks in the middle-ground, and Saseonjeong Pavilion on the cliff in the background. The Saseonbong Peaks in the center occupy an overwhelming proportion of the picture plane. However, the vertical stone pillars fail to form an organic relation and are segmented and flat. The painter of Chongseokjeong Pavilion had not yet developed a three-dimensional or natural spatial perception. The white lower and dark upper portions of the stone pillars emphasize their loftiness. The textures and cracks of the dense stone pillars were rendered by first applying light ink to the surfaces and then adding fine lines in dark ink. Here, the tip of the brush is pressed at an oblique angle and pulled down vertically, which shows an early stage of the development of axe-cut texture strokes. The contrast of black and white and use of vertical texture strokes signal the forthcoming trend toward the Zhe School painting style. Each and every contour and crack on the stone pillars is unique, which indicates an effort to accentuate their actual characteristics. The birds sitting above the stone pillars, waves, and the foam of breaking waves are all vividly described, not simply in repeated brushstrokes. The configuration of natural features shown in the above-mentioned Gyeongpodae Pavilion and Chongseokjeong Pavilion changes in other later paintings of the two scenic spots. In the Gyeongpodae Pavilion, Jukdo Island is depicted in the foreground, Gyeongpoho Lake in the middle-ground, and Gyeongpodae Pavilion and Odaesan Mountain in the background. This composition differs from the typical configuration of other Gyeongpodae Pavilion paintings from the eighteenth century that place Gyeongpodae Pavilion in the foreground and the sea in the upper section. In Chongseokjeong Pavilion, stone pillars are illustrated using a perspective viewing them from the sea, while other paintings depict them while facing upward toward the sea. These changes resulted from the established patterns of compositions used in Jeong Seon(1676~1759) and Kim Hong-do(1745~ after 1806)'s paintings of Gwandong regions. However, the configuration of the sixteenth-century Gyeongpodae Pavilion, which seemed to have no longer been used, was employed again in late Joseon folk paintings such as Gyeongpodae Pavilion in Gangneung. Famous scenic spots in the Gwandong region were painted from early on. According to historical records, they were created by several painters, including Kim Saeng(711~?) from the Goryeo Dynasty and An Gyeon(act. 15th C.) from the early Joseon period, either on a single scroll or over several panels of a folding screen or several leaves of an album. Although many records mention the production of paintings depicting sites around the Gwandong region, there are no other extant examples from this era beyond the paintings of Gyeongpodae Pavilion and Chongseokjeong Pavilion discussed in this paper. These two paintings are thought to be the earliest works depicting the Gwandong regions thus far. Moreover, they hold art historical significance in that they present information on the tradition of producing folding screens on the Gwandong region. In particular, based on the contents of the colophon written for Chongseokjeong Pavilion, the original folding screen is presumed to have consisted of eight panels. This proves that the convention of painting eight views of Gwangdong had been established by the late sixteenth century. All of the existing works mentioned as examples of sixteenth-century real scenery landscape painting show only partial elements of real scenery landscape painting since they were created as depictions of notable social gatherings or as a documentary painting for practical and/or official purposes. However, a primary objective of the paintings of Gyeongpodae Pavilion and Chongseokjeong Pavilion was to portray the ever-changing and striking nature of this real scenery. Moreover, Park Chung-gan wrote a colophon and added a poem on his admiration of the scenery he witnessed during his trip and ruminated over the true character of nature. Thus, unlike other previously known real-scenery landscape paintings, these two are of great significance as examples of real-scenery landscape paintings produced for the simple appreciation of nature. Gyeongpodae Pavilion and Chongseokjeong Pavilion are noteworthy in that they are the earliest remaining examples of the historical tradition of reflecting a sightseeing trip in painting accompanied by poetry. Furthermore, and most importantly, they broaden the understanding of Korean real-scenery landscape painting by presenting varied forms, compositions, and perspectives from sixteenth-century real-scenery landscape paintings that had formerly been unfound.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.205-225
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• 2018

Convolutional Neural Network (ConvNet) is one class of the powerful Deep Neural Network that can analyze and learn hierarchies of visual features. Originally, first neural network (Neocognitron) was introduced in the 80s. At that time, the neural network was not broadly used in both industry and academic field by cause of large-scale dataset shortage and low computational power. However, after a few decades later in 2012, Krizhevsky made a breakthrough on ILSVRC-12 visual recognition competition using Convolutional Neural Network. That breakthrough revived people interest in the neural network. The success of Convolutional Neural Network is achieved with two main factors. First of them is the emergence of advanced hardware (GPUs) for sufficient parallel computation. Second is the availability of large-scale datasets such as ImageNet (ILSVRC) dataset for training. Unfortunately, many new domains are bottlenecked by these factors. For most domains, it is difficult and requires lots of effort to gather large-scale dataset to train a ConvNet. Moreover, even if we have a large-scale dataset, training ConvNet from scratch is required expensive resource and time-consuming. These two obstacles can be solved by using transfer learning. Transfer learning is a method for transferring the knowledge from a source domain to new domain. There are two major Transfer learning cases. First one is ConvNet as fixed feature extractor, and the second one is Fine-tune the ConvNet on a new dataset. In the first case, using pre-trained ConvNet (such as on ImageNet) to compute feed-forward activations of the image into the ConvNet and extract activation features from specific layers. In the second case, replacing and retraining the ConvNet classifier on the new dataset, then fine-tune the weights of the pre-trained network with the backpropagation. In this paper, we focus on using multiple ConvNet layers as a fixed feature extractor only. However, applying features with high dimensional complexity that is directly extracted from multiple ConvNet layers is still a challenging problem. We observe that features extracted from multiple ConvNet layers address the different characteristics of the image which means better representation could be obtained by finding the optimal combination of multiple ConvNet layers. Based on that observation, we propose to employ multiple ConvNet layer representations for transfer learning instead of a single ConvNet layer representation. Overall, our primary pipeline has three steps. Firstly, images from target task are given as input to ConvNet, then that image will be feed-forwarded into pre-trained AlexNet, and the activation features from three fully connected convolutional layers are extracted. Secondly, activation features of three ConvNet layers are concatenated to obtain multiple ConvNet layers representation because it will gain more information about an image. When three fully connected layer features concatenated, the occurring image representation would have 9192 (4096+4096+1000) dimension features. However, features extracted from multiple ConvNet layers are redundant and noisy since they are extracted from the same ConvNet. Thus, a third step, we will use Principal Component Analysis (PCA) to select salient features before the training phase. When salient features are obtained, the classifier can classify image more accurately, and the performance of transfer learning can be improved. To evaluate proposed method, experiments are conducted in three standard datasets (Caltech-256, VOC07, and SUN397) to compare multiple ConvNet layer representations against single ConvNet layer representation by using PCA for feature selection and dimension reduction. Our experiments demonstrated the importance of feature selection for multiple ConvNet layer representation. Moreover, our proposed approach achieved 75.6% accuracy compared to 73.9% accuracy achieved by FC7 layer on the Caltech-256 dataset, 73.1% accuracy compared to 69.2% accuracy achieved by FC8 layer on the VOC07 dataset, 52.2% accuracy compared to 48.7% accuracy achieved by FC7 layer on the SUN397 dataset. We also showed that our proposed approach achieved superior performance, 2.8%, 2.1% and 3.1% accuracy improvement on Caltech-256, VOC07, and SUN397 dataset respectively compare to existing work.