• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.2
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• pp.769-786
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• 2016

Object recognition and object location have always drawn much interest. Also, recently various computational models have been designed. One of the big issues in this domain is the lack of an appropriate model for extracting important part of the picture and estimating the object place in the same environments that caused low accuracy. To solve this problem, a new Salient Based Bag of Visual Word (SBBoVW) model for object recognition and object location estimation is presented. Contributions lied in the present study are two-fold. One is to introduce a new approach, which is a Salient Based Bag of Visual Word model (SBBoVW) to recognize difficult objects that have had low accuracy in previous methods. This method integrates SIFT features of the original and salient parts of pictures and fuses them together to generate better codebooks using bag of visual word method. The second contribution is to introduce a new algorithm for finding object place based on the salient map automatically. The performance evaluation on several data sets proves that the new approach outperforms other state-of-the-arts.

• 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%.

• Journal of Internet Computing and Services
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• v.11 no.3
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• pp.15-24
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• 2010

In this paper, we propose an image classification which combines semantic relations of tags with contents of images to improve the satisfaction of image retrieval on application domains as huge image sharing sites. To make good use of image retrieval or classification algorithms on huge image sharing sites as Flickr, they are applicable to real tagged Web images. To classify the Web images by 'bag of visual word' based image content, our algorithm includes training the category model by utilizing the preliminary retrieved images with semantically related tags as training data and classifying the test images based on PLSA. In the experimental results on the Flickr Web images, the proposed method produced the better precision and recall rates than those from the existing method using tag information.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.1
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• pp.1-9
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• 2013

This paper propose a human action recognition method that uses bag-of-features (BoF) based on CS-LBP (center-symmetric local binary pattern) and a spatial pyramid in addition to the random forest classifier. To construct the BoF, an image divided into dense regular grids and extract from each patch. A code word which is a visual vocabulary, is formed by k-means clustering of a random subset of patches. For enhanced action discrimination, local BoF histogram from three subdivided levels of a spatial pyramid is estimated, and a weighted BoF histogram is generated by concatenating the local histograms. For action classification, a random forest, which is an ensemble of decision trees, is built to model the distribution of each action class. The random forest combined with the weighted BoF histogram is successfully applied to Standford Action 40 including various human action images, and its classification performance is better than that of other methods. Furthermore, the proposed method allows action recognition to be performed in near real-time.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.507-509
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• 2012

많은 사용자가 직접 글을 작성하고 데이터를 업로드 하는 SNS 서비스의 데이터 분류 및 분석에서 빅 데이터 활용방안이 다양하게 논의되고 있다. 특히 기존에 활용하던 텍스트 기반의 분류에서 이미지, 동영상에 대한 분류가 다양하게 시도되고 있다. 본 논문에서는 위키피디아를 이용한 이미지 태그의 의미정보를 바탕으로 플리커에서 샘플 이미지를 추출하고 이를 활용하여 'bag of visual word' 기법으로 사용자가 업로드한 이미지를 자동 분류하는 방법을 소개한다.

• Journal of the Korea Safety Management & Science
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• v.13 no.3
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• pp.29-35
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• 2011

Safety and health related information for the proper use and handling of pesticides is usually printed on the surface of the pesticide products (bottle type or bag type) in the form of texts. But, the guidelines or standards for the appropriate presentation of the texts for the pesticide products are most vague or not practical. Thus, this study aimed to provide the preliminary guidelines for the text sizes based on the legibility experiments. Total twenty subjects from two age groups (young: n=10, old: n=10, five males and five females in each group) participated in the experiment. First, subjects read the text cards presented in the distance of 50cm from the eyes of the subjects. Eight different text card sets were prepared for different font type(thick gothic-type and fine gothic-type), thickness of font(plain and bold), and number of syllables (2 and 3 syllables). When subjects read the cards, the correctness of reading (correct or wrong) was recorded and the degree of discomfort (from 1: no discomfort at all to 4: can't read at all) was also evaluated for all the text sizes. Results showed that the character size should be 4 pt or larger for the young subjects to read at least one word correctly in all the text conditions. For the old subjects to read at least one word correctly, the character size should be five pt or larder. The average of the minimum character sizes for 100% correct answer is 6.1 pt for young subjects and 10.5 pt for old subjects, respectively.

• KIISE Transactions on Computing Practices
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• v.22 no.6
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• pp.290-295
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• 2016

Recently, folksonomy-based image-sharing sites where users cooperatively make and utilize tags of image annotation have been gaining popularity. Typically, these sites retrieve images for a user request using simple text-based matching and display retrieved images in the form of photo stream. However, these tags are personal and subjective and images are not categorized, which results in poor retrieval accuracy and low user satisfaction. In this paper, we propose a categorization scheme for folksonomy images which can improve the retrieval accuracy in the tag-based image retrieval systems. Consequently, images are classified by the semantic similarity using text-information and image-information generated on the folksonomy. To evaluate the performance of our proposed scheme, we collect folksonomy images and categorize them using text features and image features. And then, we compare its retrieval accuracy with that of existing systems.