• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.1020-1042
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• 2019

Reversible data hiding in encrypted images (RDHEI) provides some real-time cloud applications; i.e. the cloud, acting as a data-hider, automatically embeds timestamp in the encrypted image uploaded by a content owner. Many existing methods of RDHEI only satisfy user privacy in which the data-hider does not know the original image, but leaks owner privacy in which the receiver can obtains the original image by decryption and extraction. In the literature, the method of Zhang et al. is the one providing weak content-owner privacy in which the content-owner and data-hider have to share a data-hiding key. In this paper, we take care of the stronger notion, called strong content-owner privacy, and achieve it by presenting a new reversible data hiding in encrypted images. In the proposed method, image decryption and message extraction are separately controlled by different types of keys, and thus such functionalities are decoupled to solve the privacy problem. At the technique level, the original image is segmented along a Hilbert filling curve. To keep image privacy, segments are transformed into an encrypted image by using random permutation. The encrypted image does not reveal significant information about the original one. Data embedment can be realized by using pixel histogram-style hiding, since this property, can be preserved before or after encryption. The proposed method is a modular method to compile some specific reversible data hiding to those in encrypted image with content owner privacy. Finally, our experimental results show that the image quality is 50.85dB when the averaged payload is 0.12bpp.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.81-86
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• 2024

Advancements in generative AI are reshaping graphic and 3D content design landscapes, where AI not only enriches graphic design but extends its reach to 3D content creation. Though 3D texture mapping through AI is advancing, AI-generated 3D modeling technology in this realm remains nascent. This paper presents AI 2D image-driven 3D modeling techniques, assessing their viability in 3D content design by scrutinizing various algorithms. Initially, four OBJ model-exporting AI algorithms are screened, and two are further evaluated. Results indicate that while AI-generated 3D models may not be directly usable, they effectively capture reference object structures, offering substantial time savings and enhanced design efficiency through manual refinements. This endeavor pioneers new avenues for 3D content creators, anticipating a dynamic fusion of AI and 3D design.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.6_2
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• pp.665-672
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• 2012

Amid rapidly increasing imagery inputs and their volume in a remote sensing imagery database, Content-Based Image Retrieval (CBIR) is an effective tool to search for an image feature or image content of interest a user wants to retrieve. It seeks to capture salient features from a 'query' image, and then to locate other instances of image region having similar features elsewhere in the image database. For a CBIR approach that uses texture as a primary feature primitive, designing a texture descriptor to better represent image contents is a key to improve CBIR results. For this purpose, an extended feature vector combining the Gabor filter and co-occurrence histogram method is suggested and evaluated for quantitywise and qualitywise retrieval performance criterion. For the better CBIR performance, assessing similarity between high dimensional feature vectors is also a challenging issue. Therefore a number of distance metrics (i.e. L1 and L2 norm) is tried to measure closeness between two feature vectors, and its impact on retrieval result is analyzed. In this paper, experimental results are presented with several CBIR samples. The current results show that 1) the overall retrieval quantity and quality is improved by combining two types of feature vectors, 2) some feature is better retrieved by a specific feature vector, and 3) retrieval result quality (i.e. ranking of retrieved image tiles) is sensitive to an adopted similarity metric when the extended feature vector is employed.

• Journal of KIISE:Databases
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• v.30 no.2
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• pp.145-156
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• 2003

Most of the content-based image retrieval systems focuses on similarity-based retrieval of natural picture images by utilizing color. shape, and texture features. For the neuroscience image databases, we found that retrieving similar images based on global average features is meaningless to pathological researchers. To realize the practical content-based retrieval on images in neuroscience databases, it is essential to represent internal contents or semantics of images in detail. In this paper, we present how to represent image contents and their related concepts to support more useful retrieval on such images. We also describe the operational semantics to support these advanced retrievals by using object-oriented message path expressions. Our schemes are flexible and extensible, enabling users to incrementally add more semantics on image contents for more enhanced content searching.

• Journal of Environmental Science International
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• v.11 no.8
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• pp.765-771
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• 2002

The current study investigated the relationship between image processed data and human visibility data for natural forest scenes from the perspective of color. In particular, the results indicated relatively low Pearson product-moment correlations between the color ratings obtained from the two methods used in the study. The first method measured the color content of slides using a image processing program, while the second used a panel of human judges to rate the color content of each slide.

• Journal of the Korea Society of Computer and Information
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• v.5 no.3
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• pp.215-219
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• 2000

The purpose of this paper is to propose a method to maximize the efficiency of a content-based image retrieval for various kinds of images. This paper discuss the self-adaptivity for the change of image domain and the self-selection of optimal features for query image, and present the efficient method to maximize content-based retrieval for various kinds of images. In this method, a content-based retrieval system is adopted to select automatically distinctive feature patterns which have a maximum efficiency of image retrieval in various kinds of images. Experimental results show that the Proposed method is improved 3% than the method using individual features.

• Journal of the Korea Society of Computer and Information
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• v.9 no.2
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• pp.19-26
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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.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.5
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• pp.473-482
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• 2008

In this paper we propose an expert system for content-based image retrieval with object database. The proposed system finds keyword by using knowledge-base and feature of extracted object, and retrieves image by using keyword based image retrieval method. The system can decrease error of image retrieval and save running time. The system also checks whether similar objects exist or not. If not, user can store information of object in object database. Proposed system is flexible and extensible, enabling experts to incrementally add more knowledge and information. Experimental results show that the proposed system is more effective than existing content-based image retrieval method in running time and precision.

• 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.

• 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.