• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.93-98
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• 2023

Instagram is one of the fastest-growing online photo social web services where users share their life images and videos with other users. Image tagging is an essential step for developing Automatic Image Annotation (AIA) methods that are based on the learning by example paradigm. Hashtags can be used on just about any social media platform, but they're most popular on Twitter and Instagram. Using hashtags is essentially a way to group together conversations or content around a certain topic, making it easy for people to find content that interests them. Practically on average, 20% of the Instagram hashtags are related to the actual visual content of the image they accompany, i.e., they are descriptive hashtags, while there are many irrelevant hashtags, i.e., stophashtags, that are used across totally different images just for gathering clicks and for search ability enhancement. Hence in this work, Sorting instagram hashtags all the way through mass tagging using HITS (Hyperlink-Induced Topic Search) algorithm is presented. The hashtags can sorted to several groups according to Jensen-Shannon divergence between any two hashtags. This approach provides an effective and consistent way for finding pairs of Instagram images and hashtags, which lead to representative and noise-free training sets for content-based image retrieval. The HITS algorithm is first used to rank the annotators in terms of their effectiveness in the crowd tagging task and then to identify the right hashtags per image.

• Journal of Korea Multimedia Society
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• v.21 no.1
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• pp.1-9
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• 2018

Much research has been conducted to judge aesthetic value for a single type of stimuli, but research to determine aesthetic value when two kinds of stimuli are presented at the same time is not explored in depth. In this paper, we measure the difference between the presentation of visual stimuli like general image and the presentation of signboard image including text stimuli using EEG. In the experiment, two oddball tasks were performed for general images and signboard images, and EEG changes according to the aesthetic value of the images were measured. As a result, the change of ERP in signboard image was larger than that of general image. We confirmed that more visual information was received and processed when two stimuli were presented at the same time.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.6_2
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• pp.585-592
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• 2013

CBIR is an effective tool to search and extract image contents in a large remote sensing image database queried by an operator or end user. However, as imaging principles are different by sensors, their visual representation thus varies among image modality type. Considering images of various modalities archived in the database, image modality difference has to be tackled for the successful CBIR implementation. However, this topic has been seldom dealt with and thus still poses a practical challenge. This study suggests a cross modality CBIR (termed as the CM-CBIR) method that transforms given query feature vector by a supervised procedure in order to link between modalities. This procedure leverages the skill of analyst in training steps after which the transformed query vector is created for the use of searching in target images with different modalities. Current initial results show the potential of the proposed CM-CBIR method by delivering the image content of interest from different modality images. Despite its retrieval capability is outperformed by that of same modality CBIR (abbreviated as the SM-CBIR), the lack of retrieval performance can be compensated by employing the user's relevancy feedback, a conventional technique for retrieval enhancement.

• Journal of Korea Multimedia Society
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• v.3 no.1
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• pp.53-62
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• 2000

Digital watermarking has been considered as an important technique to protect the copyright of digital content. It is essential that a watermark embedded in a still or moving image resists against various attacks ranging from compression, filtering to cropping. In particular, as JPEG is a dominant still image compression standard for Internet applications, digital watermarking methods that are robust against the JPEG compression are especially required. Most digital watermarking methods proposed so far work by modulating pixels/coefficients without considering the quality level of JPEG, which renders watermarks readily removable. In this paper, we proposed a new digital watermarking method as follows. For providing the robustness against JPEG, the quality level of JPEG is used as parameter on watermarking so as to compute the loss of image caused by JPEG compression and we compute the difference of an original image resulted from JPEG compression. Also in proposed method, we compute the visual components that are derived from the information of edge within image and consideration of brightness discrimination which is one of human visual system's properties. Thus, we proposed new watermarking scheme that after constructing a watermark by considering the loss of image resulted from JPEG compression and visual components derived from the human visual system, the watermark is embedded into the image.

• ETRI Journal
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• v.36 no.3
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• pp.450-459
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• 2014

In this paper, we present a new, easy-to-generate system that is capable of creating virtual 3D tree models and simulating a variety of growth processes of a tree from a single, real tree image. We not only construct various tree models with the same trunk through our proposed digital image matting method and skeleton-based abstraction of branches, but we also animate the visual growth of the constructed 3D tree model through usage of the branch age information combined with a scaling factor. To control the simulation of a tree growth process, we consider tree-growing attributes, such as branching orders, branch width, tree size, and branch self-bending effect, at the same time. Other invisible branches and leaves are automatically attached to the tree by employing parametric branch libraries under the conventional procedural assumption of structure having a local self-similarity. Simulations with a real image confirm that our system makes it possible to achieve realistic tree models and growth processes with ease.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2683-2702
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• 2015

A Content-based Image Retrieval (CBIR) system employs visual features rather than manual annotation of images. The selection of optimal features used in classification of images plays a key role in its performance. Category proliferation problem has a huge impact on performance of systems using Fuzzy Artmap (FAM) classifier. The proposed CBIR system uses a modified version of FAM called Non-Proliferation Fuzzy Artmap (NPFAM). This is developed by introducing significant changes in the learning process and the modified algorithm is evaluated by extensive experiments. Results have proved that NPFAM classifier generates a more compact rule set and performs better than FAM classifier. Accordingly, the CBIR system with NPFAM classifier yields good retrieval.

• Journal of the Semiconductor & Display Technology
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• v.7 no.2
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• pp.69-76
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• 2008

Recently, as the multimedia processing application increases rapidly by going on increasing multimedia data, the efficient retrieval method of image information is required in many fields of application and becoming the matter of major concern. Furthermore, in the last few years rapid improvements in hardware technology have made it possible to process, store and retrieve huge amounts of data in a multimedia format. As a result, Content-Based Image Retrieval (CBIR) has been receiving widespread interest during the last decade. This paper propose the content-based retrieval system as a method for performing image retrieval through the effective feature analysis of the object of significant meaning by using YCbCr channel merging on the basis of the characteristics of man's visual system.

• Journal of KIISE:Software and Applications
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• v.31 no.10
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• pp.1386-1399
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• 2004

This paper presents correlation-based automatic image captioning. Given a training set of annotated images, we want to discover correlations between visual features and textual features, so that we can automatically generate descriptive textual features for a new unseen image. We develop models with multiple design alternatives such as 1) adaptively clustering visual features, 2) weighting visual features and textual features, and 3) reducing dimensionality for noise sup-Pression. We experiment thoroughly on 10 data sets of various content styles from the Corel image database, about 680MB. The major contributions of this work are: (a) we show that careful weighting visual and textual features, as well as clustering visual features adaptively leads to consistent performance improvements, and (b) our proposed methods achieve a relative improvement of up to 45% on annotation accuracy over the state-of-the-art, EM approach.

• Journal of KIISE:Databases
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• v.36 no.5
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• pp.366-373
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• 2009

In this paper, we propose a similarity search algorithm for image databases. One of the central problems regarding content-based image retrieval (CBIR) is the semantic gap between the low-level features computed automatically from images and the human interpretation of image content. Many search algorithms used in CBIR have used the Minkowski metric (or $L_p$-norm) to measure similarity between image pairs. However those functions cannot adequately capture the aspects of the characteristics of the human visual system as well as the nonlinear relationships in contextual information. Our new search algorithm tackles this problem by employing new similarity measures and ranking strategies that reflect the nonlinearity of human perception and contextual information. Our search algorithm yields superior experimental results on a real handwritten digit image database and demonstrates its effectiveness.

• The Journal of the Korea Contents Association
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• v.12 no.9
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• pp.40-47
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• 2012

Existing art, media art, accompanied by a new path of awareness and perception instrumentalized by the human body, creating a new way to watch the interaction is proposed. Western art way to create visual images of the point cloud that represented a form that is similar to the Pointage. This traditional painting techniques using digital technology means reconfiguration. In this paper, a new appreciation of fusion of aesthetic elements and digital technology, making the point cloud in the form of video. And this holographic film projection of the spectator, and gestures to interact with the video content is presented. A Process of making contents is intent planning, content creation, content production point cloud in the form of image, 3D gestures for interaction design process, go through the process of holographic film projection. Visual and experiential content of memory recall process takes place in the consciousness of the people expressed. Complete the process of memory recall, uncertain memories, memories materialized, recalled. Uncertain remember the vague shapes of the point cloud in the form of an image represented by the image. As embodied memories through the act of interaction to manipulate images recall is complete.