• Proceedings of the IEEK Conference
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• 2000.11d
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• pp.53-56
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• 2000

In progressive image coding, if object region that have main contents in image are transmitted prior to the remained region, this method will be very useful. In this paper, the progressive image coding based on SPIHT using object region transmission method by priority is proposed. First, an original image is transformed by wavelet. Median filtering is used about wavelet transformed coefficient region for extracting object region. This extracted object region encoded by SPIHT. Then encoded object region are transmitted in advance of the remained region. This method is good to a conventional progressive image coding about entire original image. Experimental results show that the proposed method can be very effectively used for image coding applications such as internet retrieval and database searching system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.5197-5218
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• 2019

Large-scale retrieval algorithm is problem for visual analyses applications, along its research track. In this paper, we propose a high-efficiency region division-based image retrieve approaches, which fuse low-level local color histogram feature and texture feature. A novel image region division is proposed to roughly mimic the location distribution of image color and deal with the color histogram failing to describe spatial information. Furthermore, for optimizing our region division retrieval method, an image descriptor combining local color histogram and Gabor texture features with reduced feature dimensions are developed. Moreover, we propose an extended Canberra distance method for images similarity measure to increase the fault-tolerant ability of the whole large-scale image retrieval. Extensive experimental results on several benchmark image retrieval databases validate the superiority of the proposed approaches over many recently proposed color-histogram-based and texture-feature-based algorithms.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.10C
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• pp.990-999
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• 2007

In this paper, we propose the region-based image retrieval method using JSEG which is a method for unsupervised segmentation of color-texture regions. JSEG is an algorithm that discretizes an image by color classification, makes the J-image by applying a region to window mask, and then segments the image by using a region growing and merging. The segmented image from JSEG is given to a user as the query image, and a user can select a few segmented regions as the query region. After finding the MBR of regions selected by user query and generating the multiple window masks based on the center point of MBR, we extract the feature vectors from selected regions. We use the accumulated histogram as the global descriptor for performance comparison of extracted feature vectors in each method. Our approach fast and accurately supplies the relevant images for the given query, as the feature vectors extracted from specific regions and global regions are simultaneously applied to image retrieval. Experimental evidence suggests that our algorithm outperforms the recent image-based methods for image indexing and retrieval.

• Journal of Broadcast Engineering
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• v.7 no.3
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• pp.262-270
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• 2002

Although most image indexing schemes ate based on global image features, they have limited discrimination capability because they cannot capture local variations of the image. In this paper, we propose a new region-based image retrieval system that can extract important regions in the image using salient point extraction and image segmentation techniques. Our experimental results show that color and texture information in the region provide a significantly improved retrieval performances compared to the global feature extraction methods.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.18 no.2
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• pp.129-142
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• 2014

We propose a variational segmentation model based on statistical information of intensities in an image. The model consists of both a local region-based energy and a global region-based energy in order to handle misclassification which happens in a typical statistical variational model with an assumption that an image is a mixture of two Gaussian distributions. We find local ambiguous regions where misclassification might happen due to a small difference between two Gaussian distributions. Based on statistical information restricted to the local ambiguous regions, we design a local region-based energy in order to reduce the misclassification. We suggest an algorithm to avoid the difficulty of the Euler-Lagrange equations of the proposed variational model.

• Journal of Industrial Technology
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• v.20 no.B
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• pp.207-211
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• 2000

For efficient massive image retrieval, an image retrieval requires that several important objectives are satisfied, namely: automated extraction of features, efficient indexing and effective retrieval. In this work, we present a technique for extracting the 4-dimension directional feature. By directional detail, we imply strong directional activity in the horizontal, vertical and diagonal direction present in region of the image texture. This directional information also present smoothness of region. The 4-dimension feature is only indexed in the 4-D space so that complex high-dimensional indexing can be avoided.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.4
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• pp.71-79
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• 2017

Recently, Fire watching and dangerous substances monitoring system has been being developed to enhance various fire related security. It is generally assumed that fire flame extraction plays a very important role on this monitoring system. In this study, we propose the fire flame extraction method of Non-Residential Facilities based on core object extraction in image. A core object is defined as a comparatively large object at center of the image. First of all, an input image and its decreased resolution image are segmented. Segmented regions are classified as the outer or the inner region. The outer region is adjacent to boundaries of the image and the rest is not. Then core object regions and core background regions are selected from the inner region and the outer region, respectively. Core object regions are the representative regions for the object and are selected by using the information about the region size and location. Each inner region is classified into foreground or background region by comparing its values of a color histogram intersection of the inner region against the core object region and the core background region. Finally, the extracted core object region is determined as fire flame object in the image. Through experiments, we find that to provide a basic measures can respond effectively and quickly to fire in non-residential facilities.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1390-1393
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• 1987

This paper describes the image data compression based on the image decomposition. We reduced the processing time using the segmentation based on the distribution of grey level, and obtained high compression rate using the Huffman run-length coding for the segmented image, and the 2-Dimensional least square curve fitting and the shift coder for each region.

• Journal of Information Processing Systems
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• v.11 no.4
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• pp.601-615
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• 2015

Region of interest (ROI) is the most informative part of a medical image and mostly has been used as a major part of watermark. Various shapes ROIs selection have been reported in region-based watermarking techniques. In region-based watermarking schemes an image region of non-interest (RONI) is the second important part of the image and is used mostly for watermark encapsulation. In online healthcare systems the ROI wrong selection by missing some important portions of the image to be part of ROI can create problem at the destination. This paper discusses the complete medical image availability in original at destination using the whole image as a watermark for authentication, tamper localization and lossless recovery (WITALLOR). The WITALLOR watermarking scheme ensures the complete image security without of ROI selection at the source point as compared to the other region-based watermarking techniques. The complete image is compressed using the Lempel-Ziv-Welch (LZW) lossless compression technique to get the watermark in reduced number of bits. Bits reduction occurs to a number that can be completely encapsulated into image. The watermark is randomly encapsulated at the least significant bits (LSBs) of the image without caring of the ROI and RONI to keep the image perceptual degradation negligible. After communication, the watermark is retrieved, decompressed and used for authentication of the whole image, tamper detection, localization and lossless recovery. WITALLOR scheme is capable of any number of tampers detection and recovery at any part of the image. The complete authentic image gives the opportunity to conduct an image based analysis of medical problem without restriction to a fixed ROI.

• The Journal of Korea Robotics Society
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• v.8 no.2
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• pp.82-91
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• 2013

This paper proposes a building recognition algorithm using watershed image segmentation algorithm and integrated region matching (IRM). To recognize a building, a preprocessing algorithm which is using Gaussian filter to remove noise and using canny edge extraction algorithm to extract edges is applied to input building image. First, images are segmented by watershed algorithm. Next, a region adjacency graph (RAG) based on the information of segmented regions is created. And then similar and small regions are merged. Second, a color distribution feature of each region is extracted. Finally, similar building images are obtained and ranked. The building recognition algorithm was evaluated by experiment. It is verified that the result from the proposed method is superior to color histogram matching based results.