• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1349-1351
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• 2003

In this paper, theoretical channels model of Synthetic Aperture Radiometer is presented. Based on this model, how amplitude imbalance, phase imbalance and mutual coupling between the different channels effect brightness temperature image retrieving is analyzed. The computer simulation results are also presented to find out the cause of the along-track streaks usually appeared in the retrieved brightness temperature image. In addition, a new system calibration approach is introduced to solve this problem.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.7
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• pp.597-603
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• 2004

In retrieving large database of image data, the clustering is essential for fast retrieval. However, it is difficult to cluster a number of image data adequately. Moreover, current retrieval methods using similarities are uncertain of retrieval accuracy and take much retrieving time. In this paper, a suggested image retrieval system combines Fuzzy ART neural network algorithm to reinforce defects and to support them efficiently. This image retrieval system takes color and texture as specific feature required in retrieval system and normalizes each of them. We adapt Fuzzy ART algorithm as neural network which receive normalized input-vector and propose improved Fuzzy ART algorithm. The result of implementation with 200 image data shows approximately retrieval ratio of 83%.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.4
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• pp.382-387
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• 2014

Image retrieval is a procedure to find images based on the resemblance between query image and all images. In retrieving images, the crucial step that arises is how to define the similarity between images. In this paper, we propose a new similarity measure which is based on distribution of color. We apply the new measure to retrieving two different types of images, wallpaper images and the logo of automobiles, and compare its performance to other existing similarity measures.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.1
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• pp.213-231
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• 2014

Existing image search systems allow users to search images by keywords, or by example images through content-based image retrieval (CBIR). On the other hand, users might learn more relevant textual information about an image from its text captions or surrounding contexts within documents or Web pages. Without such contexts, it's difficult to extract semantic description directly from the image content. In this paper, we propose an annotation watermarking system for users to embed text descriptions, and retrieve more relevant textual information from similar images. First, tags associated with an image are converted by two-dimensional code and embedded into the image by discrete wavelet transform (DWT). Next, for images without annotations, similar images can be obtained by CBIR techniques and embedded annotations can be extracted. Specifically, we use global features such as color ratios and dominant sub-image colors for preliminary filtering. Then, local features such as Scale-Invariant Feature Transform (SIFT) descriptors are extracted for similarity matching. This design can achieve good effectiveness with reasonable processing time in practical systems. Our experimental results showed good accuracy in retrieving similar images and extracting relevant tags from similar images.

• Journal of the Institute of Convergence Signal Processing
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• v.4 no.2
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• pp.12-17
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• 2003

This paper proposes content-based image retrieval system with fuzzy ART neural network algorithm. Retrieving large database of image data, the clustering is essential for fast retrieval. However, it is difficult to cluster huge image data pertinently, Because current retrieval methods using similarities have several problems like low accuracy of retrieving and long retrieval time, a solution is necessary to complement these problems. This paper presents a content-based image retrieval system with neural network in order to reinforce abovementioned problems. The retrieval system using fuzzy ART algorithm normalizes color and texture as feature values of input data between 0 and 1, and then it runs after clustering the input data. The implemental result with 300 image data shows retrieval accuracy of approximately 87%.

• Journal of KIISE:Databases
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• v.30 no.2
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• pp.134-144
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• 2003

Spatial relationships among objects are one of the important ingredients for expressing constraints of an image in image or multimedia retrieval systems. In this paper, we propose a unified image retrieval scheme using spatial relationships among objects and their features. The proposed scheme is especially effective in computing similarity between query image and images in the database. Also, objects and their spatial relationships are captured and annotated in XML. It could give better precision and flexibility in retrieving images from database. Finally, we have implemented a prototype system for retrieving images based on proposed technique and showed some of the experiment results.

• Journal of Korea Multimedia Society
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• v.4 no.5
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• pp.423-429
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• 2001

In this paper, we propose the improved method for retrieving images with DC element of DCT that is used in image compression such as JPEG/MPEG. The existing method retrieves images with DC of fixed block size DCT. In this method, the increase in the block size results in faster retrieving speed, but it lessens the accuracy. The decrease in the block size improves the accuracy, however, it degrades the retrieving speed. In order to solve this problem, the proposed method utilizes the variable block size DCT. This method first determines the existence of object regions within each block, and then creates an image region table. Based on this table, it determines the size of each block, following a simple rule; decrease the block size in the object regions, and increase the block size in the background regions. The proposed method using variable block size DCT improves about 15% in terms of the accuracy. Additionally, when there rarely exist images of same pattern, it is able to retrieve faster only by comparing the image region patterns.

• Journal of Korea Multimedia Society
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• v.12 no.11
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• pp.1539-1546
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• 2009

According to photography, lines are important elements that make composition and mood of photo. In this paper, we proposed a measure for compositional dissimilarity between photos using lines which are basic elements of photography. To identify patterns of lines which classify composition of photos, we investigated both features of compositionally same photos and compositionally different photos. Then we developed effective measure for compositional dissimilarity between photos by applying the investigated features to the measure, and we implemented an image searching system which retrieves photo compositionally similar to given query to evaluate performance of proposed method. The searching system showed the precision of about 85% maximally for the highly matched 10 results and was capable of reliably retrieving compositionally similar to given query even if some objects were included in photos.

• Journal of Internet Computing and Services
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• v.21 no.1
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• pp.33-43
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• 2020

Retrieving a 3D model from a 3D database and augmenting the retrieved model in the Augmented Reality system simultaneously became an issue in developing the plausible AR environments in a convenient fashion. It is considered that the sketch-based 3D object retrieval is an intuitive way for searching 3D objects based on human-drawn sketches as query. In this paper, we propose a novel deep learning based approach of retrieving a sketch-based 3D object as for an Augmented Reality Model. For this work, we introduce a new method which uses Sketch CNN, Wasserstein CNN and Wasserstein center loss for retrieving a sketch-based 3D object. Especially, Wasserstein center loss is used for learning the center of each object category and reducing the Wasserstein distance between center and features of the same category. The proposed 3D object retrieval and augmentation consist of three major steps as follows. Firstly, Wasserstein CNN extracts 2D images taken from various directions of 3D object using CNN, and extracts features of 3D data by computing the Wasserstein barycenters of features of each image. Secondly, the features of the sketch are extracted using a separate Sketch CNN. Finally, we adopt sketch-based object matching method to localize the natural marker of the images to register a 3D virtual object in AR system. Using the detected marker, the retrieved 3D virtual object is augmented in AR system automatically. By the experiments, we prove that the proposed method is efficiency for retrieving and augmenting objects.

• Journal of Korea Society of Digital Industry and Information Management
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• v.5 no.4
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• pp.61-67
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• 2009

PACS and Medical Image System use only high level metadata in retrieving desired image nowadays. In order to retrieve Medical Image Data more efficiently, it would be needed to retrieve similarity by utilizing low level metadata as well as keyword retrieval by high level metadata. Thus, In this paper presents that it has realized similarity retrieval by low level metadata on the basis of MPEG-7, and keyword retrieval by high level metadata of DICOM base. It would be also available to look into medical image data in various methods and read accurate image promptly for diagnosis and treatment by retrieval with integrating two metadata.