• Journal of the Korean Society of Clothing and Textiles
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• v.46 no.3
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• pp.436-453
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• 2022

This study aims to investigate the effects of fashion information search and innovativeness of consumers on service quality of image search. Furthermore, it focuses on exploring the effect of image search service quality on use intention. Data of mobile-friendly consumers in their 20s and 30s was collected in September 2021 via an online survey. Finally, 300 samples were used for the data analysis. First, motivation for information search in fashion was divided into four factors: information, relation, leisure, and entertainment motivation. Furthermore, service quality of image search was divided into four factors: accuracy, convenience, customization, and aesthetics. Second, regression analysis was conducted to evaluate the effect of fashion information search motivation on service quality. As a result, it was found that information, relation, and entertainment motivation except leisure motivation had significant effects on all factors of service quality. Third, fashion innovativeness had no significant effect on aesthetics while consumer innovativeness significantly influenced all factors of service quality. Fourth, accuracy, convenience, and aesthetics had a significant effect on use intention.

• Journal of Sensor Science and Technology
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• v.30 no.5
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• pp.290-294
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• 2021

Experts have designed popular and successful model architectures, which, however, were not the optimal option for different scenarios. Despite the remarkable performances achieved by deep neural networks, manually designed networks for classification tasks are the backbone of object detection. One major challenge is the ImageNet pre-training of the search space representation; moreover, the searched network incurs huge computational cost. Therefore, to overcome the obstacle of the pre-training process, we introduce a network adaptation technique using a pre-trained backbone model tested on ImageNet. The adaptation method can efficiently adapt the manually designed network on ImageNet to the new object-detection task. Neural architecture search (NAS) is adopted to adapt the architecture of the network. The adaptation is conducted on the MobileNetV2 network. The proposed NAS is tested using SSDLite detector. The results demonstrate increased performance compared to existing network architecture in terms of search cost, total number of adder arithmetics (Madds), and mean Average Precision(mAP). The total computational cost of the proposed NAS is much less than that of the State Of The Art (SOTA) NAS method.

• Journal of Intelligence and Information Systems
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• v.13 no.1
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• pp.1-14
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• 2007

Character images for the background screen of cell phones are one of the fast growing sectors of the mobile content market. However, character image buyers currently experience tremendous difficulties in searching for desired images due to the awkward image search process. Content-based image retrieval (CBIR) widely used for image retrieval could be a good candidate as a solution to this problem, but it needs to overcome the limitation of the mobile Internet environment where an initial query set (IQS) cannot be easily provided as in the PC-based environment. We propose a new approach, IQS-AutoGen, which automatically generates an initial query set for CBIR on the mobile Internet. The approach applies the collaborative filtering (CF), a well-known recommendation technique, to the CBIR process by using users' preference information collected during the relevance feedback process of CBIR. The results of the experiment using a PC-based prototype system show that the proposed approach successfully satisfies the initial query requirement of CBIR in the mobile Internet environment, thereby outperforming the current image search process on the mobile Internet.

• Journal of Korea Multimedia Society
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• v.21 no.2
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• pp.158-165
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• 2018

Since a large amount of multimedia image has dramatically increased, it is important to search semantically relevant image. Thus, several semantic image annotation methods using RDF(Resource Description Framework) model in mobile environment are introduced. Earlier studies on annotating image semantically focused on both the image tag and the context-aware information such as temporal and spatial data. However, in order to fully express their semantics of image, we need more annotations which are described in RDF model. In this paper, we propose an annotation method inferencing with RDFS entailment rules and user defined rules. Our approach implemented in Moment system shows that it can more fully represent the semantics of image with more annotation triples.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.11
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• pp.870-878
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• 2013

The objective of this paper is to develop mobile-based PACS(Picture Archiving Communication System: mPACS) for diagnostic medical image service available via Android-based mobile smartphone. The proposed mPACS provides an integrated platform for mobile application of diagnostic medical images stored in hospital PACS, and allows the smartphone to store, retrieve, manipulate and transfer the diagnostic medical images. Then, the mPACS platform includes the following features for use in the Android framework (i. e., diagnostic medical image processing) : transfer protocols between PACS, mPACS and smartphone, image format converter, JPEG and JPEG2000 coders, text and avatar search, and etc. This mPACS is shown to be useful and effective in providing a solution for mobile-based diagnostic medical image service.

• Proceedings of the Korean Society of Computer Information Conference
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• 2016.01a
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• pp.33-35
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• 2016

본 논문에서는 모바일 디바이스를 이용하여 촬영한 이미지 또는 이미 모바일 디바이스에 저장된 이미지를 사용자가 검색을 위한 질의어로 사용할 수 있는 방법에 대하여 제안한다. 기존의 모바일 검색엔진을 그대로 활용하기 위해 이미지 어노테이션에 기반한 태깅 키워드를 검색 이미지와 매칭하여 질의하는 방식으로 구현하며, 이 과정에서 이미지의 분석과 분류를 위한 SVM(Support Vector Machine)과 SIFT(Scale Invariant Feature Transform) 알고리즘을 사용하였으며, 이미지 어노테이션 태깅에 대한 키워드 매칭을 위해 빅데이터에서의 MapReduce를 응용하였다.

• 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.

• Journal of the Semiconductor & Display Technology
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• v.13 no.2
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• pp.45-52
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• 2014

This paper proposes an efficient photo retrieval system that automatically indexes for searching of relevant images, using a combination of geo-coded information, direction/location of image capture device and content-based visual features. A photo image is labeled with its GPS (Global Positioning System) coordinates and direction of the camera view at the moment of capture, and the label leads to generate a geo-spatial index with three core elements of latitude, longitude and viewing direction. Then, content-based visual features are extracted and combined with the geo-spatial information, for indexing and retrieving the photo images. For user's querying process, the proposed method adopts two steps as a progressive approach, filtering the relevant subset prior to use a content-based ranking function. To evaluate the performance of the proposed scheme, we assess the simulation performance in terms of average precision and F-score, using a natural photo collection. Comparing the proposed approach to retrieve using only visual features, an improvement of 20.8% was observed. The experimental results show that the proposed method exhibited a significant enhancement of around 7.2% in retrieval effectiveness, compared to previous work. These results reveal that a combination of context and content analysis is markedly more efficient and meaningful that using only visual feature for image search.

• Journal of Mechanical Science and Technology
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• v.15 no.8
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• pp.1097-1107
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• 2001

In this paper, the wall following navigation algorithm of the mobile robot using a mono vision system is described. The key points of the mobile robot navigation system are effective acquisition of the environmental information and fast recognition of the robot position. Also, from this information, the mobile robot should be appropriately controlled to follow a desired path. For the recognition of the relative position and orientation of the robot to the wall, the features of the corridor structure are extracted using the mono vision system, then the relative position, the offset distance and steering angle of the robot from the wall, is derived for a simple corridor geometry. For the alleviation of the computation burden of the image processing, the Kalman filter is used to reduce search region in the image space for line detection. Next, the robot is controlled by this information to follow the desired path. The wall following control scheme by the PD control scheme is composed of two control parts, the approaching control and the orientation control, and each control is performed by steering and forward-driving motion of the robot. To verify the effectiveness of the proposed algorithm, the real time navigation experiments are performed. Through the result of the experiments, the effectiveness and flexibility of the suggested algorithm are verified in comparison with a pure encoder-guided mobile robot navigation system.

• Journal of the Semiconductor & Display Technology
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• v.17 no.3
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• pp.113-116
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• 2018

This paper proposes a mobile image search system which uses a sensor information of smart phone, and enables running in a variety of environments, which is implemented on Android platform. The implemented system deals with a new image descriptor using combination of the visual feature (CEDD) with EXIF attributes in the target of JPEG image, and image matching scheme, which is optimized to the mobile platform. Experimental result shows that the proposed method exhibited a significant improved searching results of around 80% in precision in the large image database. Considering the performance such as processing time and precision, we think that the proposed method can be used in other application field.