• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2633-2648
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• 2015

The problem of visual words' synonymy and ambiguity always exist in the conventional bag of visual words (BoVW) model based object category methods. Besides, the noisy visual words, so-called "visual stop-words" will degrade the semantic resolution of visual dictionary. In view of this, a novel bag of visual words method based on PLSA and chi-square model for object category is proposed. Firstly, Probabilistic Latent Semantic Analysis (PLSA) is used to analyze the semantic co-occurrence probability of visual words, infer the latent semantic topics in images, and get the latent topic distributions induced by the words. Secondly, the KL divergence is adopt to measure the semantic distance between visual words, which can get semantically related homoionym. Then, adaptive soft-assignment strategy is combined to realize the soft mapping between SIFT features and some homoionym. Finally, the chi-square model is introduced to eliminate the "visual stop-words" and reconstruct the visual vocabulary histograms. Moreover, SVM (Support Vector Machine) is applied to accomplish object classification. Experimental results indicated that the synonymy and ambiguity problems of visual words can be overcome effectively. The distinguish ability of visual semantic resolution as well as the object classification performance are substantially boosted compared with the traditional methods.

• Korean Journal of Child Studies
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• v.30 no.2
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• pp.161-177
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• 2009

In this study of the relationship between preschoolers' visual perception and reading Hangul words, the 287 participants showed significant developmental change in visual perception between three to five years of age. The researcher developed the computer-based screening Test of Visual Perception for Reading (TVPR). Factor analysis confirmed three factors of TVPR : perception of number, direction, and form constancy. These factors correlated highly with four factors of motor-reduced visual perception of the Korean Developmental Test of Visual Perception (Moon et al. 2003). All factors of TVPR explained reading real words and pseudo words; direction and form constancy perception predicted reading low frequency letters. These findings confirm that preschoolers' skills in visual perception contribute to the reading of words in Hangul.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.1
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• pp.364-380
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• 2016

The most popular approach in object classification is based on the bag of visual-words model, which has several fundamental problems that restricting the performance of this method, such as low time efficiency, the synonym and polysemy of visual words, and the lack of spatial information between visual words. In view of this, an object classification based on weakly supervised E2LSH and saliency map weighting is proposed. Firstly, E2LSH (Exact Euclidean Locality Sensitive Hashing) is employed to generate a group of weakly randomized visual dictionary by clustering SIFT features of the training dataset, and the selecting process of hash functions is effectively supervised inspired by the random forest ideas to reduce the randomcity of E2LSH. Secondly, graph-based visual saliency (GBVS) algorithm is applied to detect the saliency map of different images and weight the visual words according to the saliency prior. Finally, saliency map weighted visual language model is carried out to accomplish object classification. Experimental results datasets of Pascal 2007 and Caltech-256 indicate that the distinguishability of objects is effectively improved and our method is superior to the state-of-the-art object classification methods.

• Journal of the Semiconductor & Display Technology
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• v.18 no.4
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• pp.57-61
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• 2019

Nowadays, portable digital cameras such as smart phone cameras are being popularly used for entertainment and visual information recording. Given a database of geo-tagged images, a visual location recognition system can determine the place depicted in a query photo. One of the most common visual location recognition approaches is the bag-of-words method where local image features are clustered into visual words. In this paper, we propose a new bag-of-words-based visual location recognition algorithm using time-series streetview database. The proposed algorithm selects only a small subset of image features which will be used in image retrieval process. By reducing the number of features to be used, the proposed algorithm can reduce the memory requirement of the image database and accelerate the retrieval process.

• Journal of the Korea Fashion and Costume Design Association
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• v.17 no.4
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• pp.91-100
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• 2015

The purpose of this study is to analyze the style of check dresses shown in collections from 2011 to 2014 and to extract main expressional words for the development of semantic differential scales of visual images according to the change in silhouette of block check dresses. The results of this study are as follows: 1) 120 check dresses shown in collections were composed of 57 straight silhouette dresses, 38 fitted silhouette dresses, 23 hourglass silhouette dresses, 1 barrel silhouette dress, and 1 atypical silhouette dress. And check pattern mostly used in the current collections a square pattern of block check, tartan check that is a Scotch traditional lattice pattern, a small lattice pattern of gingham check, over check that other check patterns are arranged on check pattern, star-shaped hound tooth check, glen check mixing small pattern and big pattern. The visual image for check dress differs according to changes in the check pattern and silhouette of the dress. 2) Main expressional words of visual images for block check dresses differ greatly depending on the silhouette of dresses. The visual images are ranked in the order of 'graphic', 'simple', 'hard', 'modern' for straight silhouette of block check dresses. The words of 'lively', 'girlish', 'feminine', 'cute' are ranked for hourglass silhouette of block check dresses. And the words of 'confident', 'feminine', 'modern' are marked down for fitted silhouette of block check dresses.

• Journal of Fashion Business
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• v.13 no.6
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• pp.137-146
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• 2009

This study analyzed hierarchy of image for visual evaluation of flare skirt. This study analyzed expression words about flare skirt with frequency data of image expression words with different length and volume of flare. Stimuli for the study were set to be 4 different volume of flare ($90^{\circ}$, $180^{\circ}$, $270^{\circ}$, $360^{\circ}$) and 3 different length of skirt(48cm, 58cm, 68cm). Stimuli were made by using I-Designer which is Virtual Sewing System. From simulation of flare skirt, the subjects were asked to write down suggested adjective freely and selected 210 adjectives. With this, we chose total 38 adjectives considering frequencies in the pre-study. And we analyzed the combination process of expression words according to construction factor of flare skirt and hierarchy of image from dendrogram which was resulted by hierarchical cluster analysis. 'Feminine' got high score in all 12 flare skirt. When the skirt was short, it was vivid, and as the skirt got longer, ordinary and pure image showed. Also, as the volume of flare got larger, the average of visual effect was higher than visual image. Visual hierarchy construction according to construction factors of flare skirt could be divided into visual image and visual effect, and visual image was shown to be form 'A type - large volume of flare and short skirt length', 'H type-small volume of flare and short skirt length' and 'X type - large volume of flare and long skirt length'.

• Journal of the Korea Fashion and Costume Design Association
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• v.14 no.2
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• pp.147-156
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• 2012

The purpose of this study is to analyze the style of wide pants shown in collections from 2008 to 2011 and to extract main expressional words for the development of semantic differential scales of visual images according to the change in silhouette of wide pants. The results of this study are as follows: 1) The wide pants which women wore in the 1970s were similar to men's. The aesthetic values for the wide pants included the social women's requests of the time. On the other hand, new wide pants shown in the current collections have diversified by adding designers' will to express contemporary women's tastes and fashion senses. 2) 742 wide pants shown in collections were composed of 459 straight, 147 bell-bottom and 136 flared pants. The design differs according to changes in the waist position and width of the wide pants. 3) Main expressional words of visual images for wide pants differ greatly depending on the silhouette of wide pants. The visual images are ranked in the order of 'showed that legs are long', 'looked taller', 'neat', 'relaxed', 'retro', 'modern' for straight pants. The words of 'retro', 'countrified', 'legs seemed to be long', 'enough' 'confident' 'looked like thighs that are slim' are ranked for bell-bottom pants. And the words of 'plentiful' 'loose', 'enough', 'retro' 'uncomfortable', 'relaxed', 'countrified' are marked down for flared pants.

• The Journal of the Society of Korean Medicine Diagnostics
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• v.15 no.3
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• pp.235-244
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• 2011

Objectives: There are four types of diagnostic methods in Oriental medicine, and visual inspection is the first method among them. This study was written in order to complement further understanding on visual inspection. Methods: The authors reviewed a word related with visual inspection in Nei-Ching. The authors researched static words such as bigger/smaller, longer/shorter, slower/faster, curved/straight, one-sided/fair, and groove/uplift, and active words such as extension and contraction, shake, tremor, slow, fast, walk, run, standing, lying, and sitting related with visual inspection in Nei-Ching. Results: The static words linked with visual inspection are related with skin, muscles, fat, and especially the liver, stomach, and large intestine. The active words linked with visual inspection are related with movement of muscles, fat, and bone. Conclusion: In this study, the authors provided further understanding on visual inspection in Nei-Ching. However, there was no clear reference point about appearances and movements.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.12
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• pp.547-552
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• 2014

As social multimedia sites are getting popular such as Flickr and Facebook, the amount of image information has been increasing very fast. So there have been many studies for accurate social image retrieval. Some of them were web image classification using semantic relations of image tags and BoVW(Bag of Visual Words). In this paper, we propose a method to detect salient region in images using GBVS(Graph Based Visual Saliency) model which can eliminate less important region like a background. First, We construct BoVW based on SIFT algorithm from the database of the preliminary retrieved images with semantically related tags. Second, detect salient region in test images using GBVS model. The result of image classification showed higher accuracy than the previous research. Therefore we expect that our method can classify a variety of images more accurately.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.79-82
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• 2015

Bag of visual words(BoVW) is one of the image classification and retrieval methods, using feature point that automatical sorting and searching system by image feature vector of data base. The existing method using feature point shall search or classify the image that user unwanted. To solve this weakness, when comprise the words, include not only feature point but color information that express overall mood of image or texture information that express repeated pattern. It makes various searching possible. At the test, you could see the result compared between classified image using the words that have only feature point and another image that added color and texture information. New method leads to accuracy of 80~90%.