• Title/Summary/Keyword: Image search

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Binary Visual Word Generation Techniques for A Fast Image Search (고속 이미지 검색을 위한 2진 시각 단어 생성 기법)

  • Lee, Suwon
    • Journal of KIISE
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    • v.44 no.12
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    • pp.1313-1318
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    • 2017
  • Aggregating local features in a single vector is a fundamental problem in an image search. In this process, the image search process can be speeded up if binary features which are extracted almost two order of magnitude faster than gradient-based features are utilized. However, in order to utilize the binary features in an image search, it is necessary to study the techniques for clustering binary features to generate binary visual words. This investigation is necessary because traditional clustering techniques for gradient-based features are not compatible with binary features. To this end, this paper studies the techniques for clustering binary features for the purpose of generating binary visual words. Through experiments, we analyze the trade-off between the accuracy and computational efficiency of an image search using binary features, and we then compare the proposed techniques. This research is expected to be applied to mobile applications, real-time applications, and web scale applications that require a fast image search.

Analysis of Cultural Context of Image Search with Deep Transfer Learning (심층 전이 학습을 이용한 이미지 검색의 문화적 특성 분석)

  • Kim, Hyeon-sik;Jeong, Jin-Woo
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.24 no.5
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    • pp.674-677
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    • 2020
  • The cultural background of users utilizing image search engines has a significant impact on the satisfaction of the search results. Therefore, it is important to analyze and understand the cultural context of images for more accurate image search. In this paper, we investigate how the cultural context of images can affect the performance of image classification. To this end, we first collected various types of images (e.g,. food, temple, etc.) with various cultural contexts (e.g., Korea, Japan, etc.) from web search engines. Afterwards, a deep transfer learning approach using VGG19 and MobileNetV2 pre-trained with ImageNet was adopted to learn the cultural features of the collected images. Through various experiments we show the performance of image classification can be differently affected according to the cultural context of images.

An Empirical Evaluation of Color Distribution Descriptor for Image Search (이미지 검색을 위한 칼라 분포 기술자의 성능 평가)

  • Lee, Choon-Sang;Lee, Yong-Hwan;Kim, Young-Seop;Rhee, Sang-Burm
    • Journal of the Semiconductor & Display Technology
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    • v.5 no.2
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    • pp.27-31
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    • 2006
  • As more and more digital images are made by various applications, image retrieval becomes a primary concern in technology of multimedia. This paper presents color based descriptor that uses information of color distribution in color images which is the most basic element for image search and performance of proposed visual feature is evaluated through the simulation. In designing the image search descriptor used color histogram, HSV, Daubechies 9/7 and 2 level wavelet decomposition provide better results than other parameters in terms of computational time and performances. Also histogram quadratic matrix outperforms the sum of absolute difference in similarity measurements, but spends more than 60 computational times.

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The effect of image search, social influence characteristics and anthropomorphism on purchase intention in mobile shopping

  • KIM, Won-Gu;PARK, Hyeonsuk
    • The Journal of Industrial Distribution & Business
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    • v.11 no.6
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    • pp.41-53
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    • 2020
  • Purpose: The purpose of this study is to review the previous studies on the characteristics of the image search service provided by using artificial intelligence, the social impact characteristics, and the moderating effect of perceived anthropomorphism, and conduct empirical analysis to identify the constituent factors affecting purchase intention. To clarify. Through this, I tried to present theoretical and practical implications. Research design, data, and methodology: Research design was that characteristics of image search service (ubiquity and information quality) and social impact characteristics (subjective norms, electronic word of mouth marketing) are affected by mediation of satisfaction and flow, therefore, control of perceived anthropomorphism have an effect on purchase intention to increase. For analysis, research conducted literature review, and developed questionnaires, so that EM firm which is a specialized research institute has collected data. This was conducted on 410 people between the 20s and 50s who have mobile shopping experiences. SPSS Statistics 23 and AMOS 23 had been used to perform necessary analysis such as exploratory factor analysis, reliability analysis, feasibility analysis, and structural equation modeling based on this data. Results: first, ubiquity, information quality and subjective norms were found to have a positive effect on purchase intention through satisfaction and flow parameters. Second, satisfaction and flow were found to have a mediating effect between ubiquity, information quality, and subjective norms and purchase intentions. However, there was no mediating effect between eWOM information and purchase intention. Third, perceived anthropomorphism was found to have a moderating effect between information quality and satisfaction, and it was found that there was no moderating effect on the relationship between information quality and flow. Conclusions: The information quality of image search services using artificial intelligence has a positive effect on satisfaction, and it has been found that there is a positive moderate effect of perceived anthropomorphism in this relationship, which may be an academic contribution to the distribution science utilizing artificial intelligence. Therefore, it is possible to propose a distribution strategy that improves purchase intention by utilizing image search service and anthropomorphism in practical business and providing a more enjoyable immersive experience to customers.

Evaluation of the Use of Color Distribution Image Search in Various Setup (칼라 분포정보를 이용한 성능적 이미지 검색 평가)

  • Lee, Yong-Hwan;Ahn, Hyo-Chang;Rhee, Sang-Burm;Park, Jin-Yang
    • Journal of the Korea Computer Industry Society
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    • v.7 no.5
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    • pp.537-544
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    • 2006
  • Image Search is one of the most exciting and fast growing research areas in the filed of multimedia technology. This paper conducts an empirical evaluation of color descriptor that uses the information of color distribution in color images, which is the most basic element for image search. With the experimental results, we observe that in the top 10% of precision, HSV, Daubechies 9/7 and 2 level decomposition have little better than others. Also histogram quadratic metrics outperform the Minkowski form distance metrics in similarity measurements, but spend more than 20 in computational times.

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The Suggestion of Particular Area Image Search Method (부분 영역 이미지 검색 방법의 제안)

  • Kim, Sungkon
    • The Journal of the Convergence on Culture Technology
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    • v.4 no.4
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    • pp.355-360
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    • 2018
  • We propose a method to connect partial image in whole image to partial image in whole image of other internet site. For this study, we have developed four partial image generation methods and retrieval methods. A method of 'image segmentation' that cuts out only partial images that want to provide information from the whole image, a method of 'creating an image block' that finds outermost points of a cut-out partial image, a method of 'Stamp transformation of outer points', which connects outer points and registers them as the most similar image stamp, and a retrieval method that connects image stamps with other image stamps are developed. We suggested a image search UI that can use image stamps in various ways.

Comparison and Evaluation of Web-based Image Search Engines (이미지정보 탐색을 위한 웹 검색엔진의 비교 평가)

  • Kim, Hyo-Jung
    • Journal of Information Management
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    • v.31 no.4
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    • pp.50-70
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    • 2000
  • Since the contents of internet resources are beginning to include texts, images and sounds, different Web-based image search engines have been developed accordingly. It is a fact that these diversities of multimedia contents have made search process and retrieval of relevant information very difficult. The purpose of the study is to compare and evaluate its special features and performance of the existing image search engines in order to provide user help to select appropriate search engines. The study selected AV Photo Finder, Lycos MultiMedia, Amazing Picture Machine, Image Surfer, WebSeek, Ditto for comparison and evaluation because of their reputations of popularity among users of image search engines. The methodology of the study was to analyze previous related literature and establish criteria for the evaluation of image search engines. The study investigated characteristics, indexing methods, search capabilities, screen display and user interfaces of different search engines for the purpose of comparison of its performance. Finally, the study measured relative recall and precision ratios to evaluate their electiveness of retrieval under the experimental set up. Results of the comparative analysis in regard to its search performance are as follows. AV Photo Finder marked the highest rank among other image search engines. Ditto and WebSeek also showed comparatively high precision ratio. Lycos MultiMedia and Image Surfer follows after them. Amazing Picture Machine stowed the lowest in ranking.

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A Study on Increasing the Efficiency of Image Search Using Image Attribute in the area of content-Based Image Retrieval (내용기반 이미지 검색에 있어 이미지 속성정보를 활용한 검색 효율성 향상)

  • Mo, Yeong-Il;Lee, Cheol-Gyu
    • Journal of the Korea Society for Simulation
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    • v.18 no.2
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    • pp.39-48
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    • 2009
  • This study reviews the limit of image search by considering on the image search methods related to content-based image retrieval and suggests a user interface for more efficient content-based image retrieval and the ways to utilize image properties. For now, most studies on image search are being performed focusing on content-based image retrieval; they try to search based on the image's colors, texture, shapes, and the overall form of the image. However, the results are not satisfactory because there are various technological limits. Accordingly, this study suggests a new retrieval system which adapts content-based image retrieval and the conventional keyword search method. This is about a way to attribute properties to images using texts and a fast way to search images by expressing the attribute of images as keywords and utilizing them to search images. Also, the study focuses on a simulation for a user interface to make query language on the Internet and a search for clothes in an online shopping mall as an application of the retrieval system based on image attribute. This study will contribute to adding a new purchase pattern in online shopping malls and to the development of the area of similar image search.

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Categorizing Web Image Search Results Using Emotional Concepts (감성 개념을 이용한 웹 이미지 검색 결과 분류)

  • Kim, Young-Rae;Kwon, Kyung-Su;Shin, Yun-Hee;Kim, Eun-Yi
    • 한국HCI학회:학술대회논문집
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    • pp.562-566
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    • 2009
  • In this paper, we present a novel system to categorize web image search results using emotional concepts and to browse the results more conveniently and easily. The proposed system can categorize search results into 8 emotional categories based on emotion vector, which obtained by color and pattern features. Here, we use Kobayashi’s emotional categories: {romantic, natural, casual, elegant, chic, classic, dandy and modern}. With search results for a given query, the proposed system can provide categorized images for each emotional category. With 1,000 Yahoo! search images, we compared the proposed method with Yahoo! image search engine in respect of satisfaction, efficiency, convenience and relevance with a user study. Our experimental results show the effectiveness of the proposed method.

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Clustering Representative Annotations for Image Browsing (이미지 브라우징 처리를 위한 전형적인 의미 주석 결합 방법)

  • Zhou, Tie-Hua;Wang, Ling;Lee, Yang-Koo;Ryu, Keun-Ho
    • Proceedings of the Korean Information Science Society Conference
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    • pp.62-65
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    • 2010
  • Image annotations allow users to access a large image database with textual queries. But since the surrounding text of Web images is generally noisy. an efficient image annotation and retrieval system is highly desired. which requires effective image search techniques. Data mining techniques can be adopted to de-noise and figure out salient terms or phrases from the search results. Clustering algorithms make it possible to represent visual features of images with finite symbols. Annotationbased image search engines can obtains thousands of images for a given query; but their results also consist of visually noise. In this paper. we present a new algorithm Double-Circles that allows a user to remove noise results and characterize more precise representative annotations. We demonstrate our approach on images collected from Flickr image search. Experiments conducted on real Web images show the effectiveness and efficiency of the proposed model.

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