• Journal of the Korea Society of Computer and Information
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• v.14 no.6
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• pp.65-71
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• 2009

The basic features in the indexing and retrieval of the image is used color, shape, and texture in traditional image retrieval method. We do not use these features and offers a new way. For content-based video indexing and retrieval, visual features used to measure the similarity of the geometric method is presented. This method is called the Radon transform. Without separation, this method is calculated based on the geometric distribution of image. In the experiment has a very good search results.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.553-556
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• 2003

In the field of content-based image retrieval, various mathematical low-level features have been proposed to describe the perceptual content of images. Since most of the features are assumed to be independent of each other, one feature is extracted from images without any consideration of the other features. Recently proposed CCE and SCFT taking advantage of the correlation between color and texture have shown relatively good performance. In this paper, the performance of CCE, SCFT, and the traditional regular weighted comparison method are evaluated. Simulation results with natural images have shown that CCE outperforms the other methods.

• Proceedings of the Korean Society of Computer Information Conference
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• 2014.01a
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• pp.467-468
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• 2014

본 논문에서는 파일 내용 기반 검색 프로그램을 설계하고 구현하였다. 역 인덱스 구조를 이용하여 설계하였으며 별도의 정보 검색 라이브러리 사용 없이 구현하였다. 인덱스 파일은 트라이 자료 구조를 직접 설계 및 구현 하였으며 자바 언어의 HashMap 구조를 중첩 형태로 구현하였다. 개발 시스템의 유용성을 테스트하기 위해 GRE 단어집에 수록된 약 3,300개의 단어를 사용하여 임의 생성한 텍스트 파일 집합을 사용하였다.

• Journal of Internet Computing and Services
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• v.4 no.4
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• pp.1-8
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• 2003

As the multimedia information retrieval system is advanced, the study of multimedia information retrieval is changing the method of low-level content based image retrieval to the semantical concept based retrieval. in this paper, we apply the theory of ontology to overcome the conceptual heterogeneity in the annotation based image retrieval. And we solve the some problems that happen when the ontology apply. As a result of our study, we try to apply the domain ontology to settle the conceptual heterogenity. In the experimental result, we knew that the semantic distance among the words is pretty dose when we apply the domain ontology than the wordnet. And in this paper, we show the possibility of the semantic image retrieval as we apply the domain ontology in the annotation based image retrieval.

• Journal of Information Processing Systems
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• v.15 no.4
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• pp.737-747
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• 2019

Shape description is an important and fundamental issue in content-based image retrieval (CBIR), and a number of shape description methods have been reported in the literature. For shape description, both global information and local contour variations play important roles. In this paper a new included-angular ternary pattern (IATP) based shape descriptor is proposed for shape image retrieval. For each point on the shape contour, IATP is derived from its neighbor points, and IATP has good properties for shape description. IATP is intrinsically invariant to rotation, translation and scaling. To enhance the description capability, multiscale IATP histogram is presented to describe both local and global information of shape. Then multiscale IATP histogram is combined with included-angular histogram for efficient shape retrieval. In the matching stage, cosine distance is used to measure shape features' similarity. Image retrieval experiments are conducted on the standard MPEG-7 shape database and Swedish leaf database. And the shape image retrieval performance of the proposed method is compared with other shape descriptors using the standard evaluation method. The experimental results of shape retrieval indicate that the proposed method reaches higher precision at the same recall value compared with other description method.

• Journal of Internet Computing and Services
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• v.2 no.2
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• pp.51-63
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• 2001

In this paper, we design and implement a XML document retrieval system to support structure-based retrieval using the BRS/Search system that is a commercial search engine, The implemented system in this paper represents the logical structure of XML documents as the directory structure of the Unix file system. In addition, we define the database schema of BRS/Search system to store documents, We also implement a ETID extractor, a structure information extractor, a storage manager and a query processor additionally to support content retrieval, structure retrieval, mixed retrieval and attribute retrieval in the BRS/Search system.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.87-96
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• 2021

The demand for e-learning through video lectures is rapidly increasing due to its diverse advantages over the traditional learning methods. This led to massive volumes of web-based lecture videos. Indexing and retrieval of a lecture video or a lecture video topic has thus proved to be an exceptionally challenging problem. Many techniques listed by literature were either visual or audio based, but not both. Since the effects of both the visual and audio components are equally important for the content-based indexing and retrieval, the current work is focused on both these components. A framework for automatic topic-based indexing and search depending on the innate content of the lecture videos is presented. The text from the slides is extracted using the proposed Merged Bounding Box (MBB) text detector. The audio component text extraction is done using Google Speech Recognition (GSR) technology. This hybrid approach generates the indexing keywords from the merged transcripts of both the video and audio component extractors. The search within the indexed documents is optimized based on the Naïve Bayes (NB) Classification and K-Means Clustering models. This optimized search retrieves results by searching only the relevant document cluster in the predefined categories and not the whole lecture video corpus. The work is carried out on the dataset generated by assigning categories to the lecture video transcripts gathered from e-learning portals. The performance of search is assessed based on the accuracy and time taken. Further the improved accuracy of the proposed indexing technique is compared with the accepted chain indexing technique.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.40-48
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• 2021

Content-Based Image Retrieval (CBIR) system plays a vital role to retrieve the relevant images as per the user perception from the huge database is a challenging task. Images are represented is to employ a combination of low-level features as per their visual content to form a feature vector. To reduce the search time of a large database while retrieving images, a novel image retrieval technique based on feature dimensionality reduction is being proposed with the exploit of metaheuristic optimization techniques based on Genetic Algorithm (GA), Extended Binary Cuckoo Search (EBCS) and Whale Optimization Algorithm (WOA). Each image in the database is indexed using a feature vector comprising of fuzzified based color histogram descriptor for color and Median binary pattern were derived in the color space from HSI for texture feature variants respectively. Finally, results are being compared in terms of Precision, Recall, F-measure, Accuracy, and error rate with benchmark classification algorithms (Linear discriminant analysis, CatBoost, Extra Trees, Random Forest, Naive Bayes, light gradient boosting, Extreme gradient boosting, k-NN, and Ridge) to validate the efficiency of the proposed approach. Finally, a ranking of the techniques using TOPSIS has been considered choosing the best feature selection technique based on different model parameters.

• The KIPS Transactions:PartD
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• v.13D no.1 s.104
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• pp.29-36
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• 2006

In this paper, we present a leaf image retrieval system that represents and retrieves leaf images based on their shape. For more effective representation of leaf images, we improved an existing MPP algorithm. Also, in order to reduce the response time, we proposed a new dynamic matching algorithm at basically revises the Nearest Neighbor search. The system provides users with an interface for uploading query images or tools to generate queries based on shape features and retrieves images based on their similarity. For convenience, users are allowed to easily query images by sketching leaf shape or leaf arrangement on the web. In the experiment, we constructed an image database of Korean native plants and measured the system performance by counting the number of similar images retrieved for queries.

• KIEE International Transaction on Systems and Control
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• v.12D no.1
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• pp.1-6
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• 2002

In this study, we propose the new color-based image retrieval technique using the representative colors of images and their ratios to a total image size obtained through color segmentation in HSV color space. Color information of an image is described by reconstructing the color histogram of an image through Gaussian modelling to its representative colors and ratios. And the similarity between two images is measured by histogram intersection. The proposed method is compared with the existing methods by performing retrieval experiments for various 1280 trademark image database.