• Title/Summary/Keyword: Content-based Image Retrieval

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Interactive Genetic Algorithm for Content-based Image Retrieval

  • Lee, Joo-Young;Cho, Sung-Bae
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • pp.479-484
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    • 1998
  • As technology in a computer hardware and software advances, efficient information retrieval from multimedia database gets highly demanded. Recently, it has been actively exploited to retrieve information based on the stored contents. However, most of the methods emphasize on the points which are far from human intuition or emotion. In order to overcome this shortcoming , this paper attempts to apply interactive genetic algorithm to content-based image retrieval. A preliminary result with subjective test shows the usefulness of this approach.

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Content Based Image Retrieval Using Combined Features of Shape, Color and Relevance Feedback

  • Mussarat, Yasmin;Muhammad, Sharif;Sajjad, Mohsin;Isma, Irum
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.7 no.12
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    • pp.3149-3165
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    • 2013
  • Content based image retrieval is increasingly gaining popularity among image repository systems as images are a big source of digital communication and information sharing. Identification of image content is done through feature extraction which is the key operation for a successful content based image retrieval system. In this paper content based image retrieval system has been developed by adopting a strategy of combining multiple features of shape, color and relevance feedback. Shape is served as a primary operation to identify images whereas color and relevance feedback have been used as supporting features to make the system more efficient and accurate. Shape features are estimated through second derivative, least square polynomial and shapes coding methods. Color is estimated through max-min mean of neighborhood intensities. A new technique has been introduced for relevance feedback without bothering the user.

An Effective Similarity Measure for Content-Based Image Retrieval using MPEG-7 Dominant Color Descriptor (내용기반 이미지 검색을 위한 MPEG-7 우위컬러 기술자의 효과적인 유사도)

  • Lee, Jong-Won;Nang, Jong-Ho
    • Journal of KIISE:Computing Practices and Letters
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    • v.16 no.8
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    • pp.837-841
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    • 2010
  • This paper proposes an effective similarity measure for content-based image retrieval using MPEG-7 DCD. The proposed method can measure the similarity of images with the percentage of dominant colors extracted from images. As the result of experiments, we achieved a significant improvement of 18.92% with global DCD and 47.22% with local DCD in ANMRR than the result by QHDM. This result shows that the proposed method is an effective similarity measure for content-based image retrieval. Especially, our method is useful for region-based image retrieval.

Improvement of Content-based Image Retrieval by Considering Image Editing Effect (영상편집효과를 고려한 내용기반 영상 검색의 개선에 관한 연구)

  • Kang Seok-Jun;Bae Tae-Meon;Kim Ki-Hyun;Han Seung-Wan;Jeong Chi-Yoon;Nam Tae-Yong;Ro Yong-Man
    • Journal of Korea Multimedia Society
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    • v.9 no.5
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    • pp.564-575
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    • 2006
  • With the rapid increase of the number of multimedia contents, people consume a lot of multimedia contents through various distribution channels. Content-based image retrieval uses visual features that represent the contents of images. And users can retrieve or filter images based on the contents of the images using the features. But, the editing of the multimedia contents distorts the original visual features of the multimedia contents, thereby the performance of content-based image retrieval system could be lowered. In this paper, we describe the image editing effects that lower the performance of the retrieval system and propose algorithms that can remove the image editing effect and improve content-based image retrieval system.

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Deep Hashing for Semi-supervised Content Based Image Retrieval

  • Bashir, Muhammad Khawar;Saleem, Yasir
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.12 no.8
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    • pp.3790-3803
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    • 2018
  • Content-based image retrieval is an approach used to query images based on their semantics. Semantic based retrieval has its application in all fields including medicine, space, computing etc. Semantically generated binary hash codes can improve content-based image retrieval. These semantic labels / binary hash codes can be generated from unlabeled data using convolutional autoencoders. Proposed approach uses semi-supervised deep hashing with semantic learning and binary code generation by minimizing the objective function. Convolutional autoencoders are basis to extract semantic features due to its property of image generation from low level semantic representations. These representations of images are more effective than simple feature extraction and can preserve better semantic information. Proposed activation and loss functions helped to minimize classification error and produce better hash codes. Most widely used datasets have been used for verification of this approach that outperforms the existing methods.

The Design of Adaptive Component Analysis System for Image Retrieval (영상 검색을 위한 적응적 컴포넌트 분석 시스템 설계)

  • 최철;박장춘
    • KSCI Review
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    • v.12 no.1
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    • pp.9-19
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    • 2004
  • This paper proposes ACA (Adaptive Component Analysis) as a method for feature extraction and analysis of the content-based image retrieval system. For satisfactory retrieval, the features extracted from images should be appropriately applied according to the image domains and for this. retrieval measurement is Proposed in this study. Retrieval measurement is a standard indicating how important the value of a relevant feature is to image retrieval ACA is a middle stage for content-based image retrieval and it purposes to improve the retrieval speed and performance.

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Genetic Algorithm based Relevance Feedback for Content-based Image Retrieval

  • Seo, Kwang-Kyu
    • Journal of the Semiconductor & Display Technology
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    • v.7 no.4
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    • pp.13-18
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    • 2008
  • This paper explores a content-based image retrieval framework with relevance feedback based on genetic algorithm (GA). This framework adopts GA to learn the user preferences using the similarity functions defined for all available descriptors. The objective of the GA-based learning methods is to learn the user preferences using the similarity functions and to find a descriptor combination function that best represents the user perception. Experiments were performed to validate the proposed frameworks. The experiments employed the natural image databases and color and texture descriptors to represent the content of database images. The proposed frameworks were compared with the other two relevance feedback methods regarding effectiveness in image retrieval tasks. Experiment results demonstrate the superiority of the proposed method.

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Anatomy of Current Issues on Content-Based Image Retrieval (내용기반 영상검색 시스템의 분석 및 발전 방안)

  • Singh, Kulwinder;Ma, Ming;Park, DongWon;An, Syungog
    • The Journal of Korean Association of Computer Education
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    • v.6 no.4
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    • pp.31-36
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    • 2003
  • In the past few years, enormous improvements have been obtained in the field of content-based image retrieval (CBIR). This paper presents a comprehensive survey on the current CBIR systems and some of their challenging technical aspects, which stand as an obstacle on its way to become successful. Furthermore, we have focused on the current state of semantic image retrieval and also we have suggested future promising directions for further research.

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A Novel Image Classification Method for Content-based Image Retrieval via a Hybrid Genetic Algorithm and Support Vector Machine Approach

  • Seo, Kwang-Kyu
    • Journal of the Semiconductor & Display Technology
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    • v.10 no.3
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    • pp.75-81
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    • 2011
  • This paper presents a novel method for image classification based on a hybrid genetic algorithm (GA) and support vector machine (SVM) approach which can significantly improve the classification performance for content-based image retrieval (CBIR). Though SVM has been widely applied to CBIR, it has some problems such as the kernel parameters setting and feature subset selection of SVM which impact the classification accuracy in the learning process. This study aims at simultaneously optimizing the parameters of SVM and feature subset without degrading the classification accuracy of SVM using GA for CBIR. Using the hybrid GA and SVM model, we can classify more images in the database effectively. Experiments were carried out on a large-size database of images and experiment results show that the classification accuracy of conventional SVM may be improved significantly by using the proposed model. We also found that the proposed model outperformed all the other models such as neural network and typical SVM models.

Content-Based Image Retrieval Using Multi-Resolution Multi-Direction Filtering-Based CLBP Texture Features and Color Autocorrelogram Features

  • Bu, Hee-Hyung;Kim, Nam-Chul;Yun, Byoung-Ju;Kim, Sung-Ho
    • Journal of Information Processing Systems
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    • v.16 no.4
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    • pp.991-1000
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    • 2020
  • We propose a content-based image retrieval system that uses a combination of completed local binary pattern (CLBP) and color autocorrelogram. CLBP features are extracted on a multi-resolution multi-direction filtered domain of value component. Color autocorrelogram features are extracted in two dimensions of hue and saturation components. Experiment results revealed that the proposed method yields a lot of improvement when compared with the methods that use partial features employed in the proposed method. It is also superior to the conventional CLBP, the color autocorrelogram using R, G, and B components, and the multichannel decoded local binary pattern which is one of the latest methods.