• 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 Multimedia Information System
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• v.3 no.4
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• pp.155-160
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• 2016

The Pulse Coupled Neural Network (PCNN) is a kind of neural network models that consists of spiking neurons and local connections. The PCNN was originally proposed as a model that can reproduce the synchronous phenomena of the neurons in the cat visual cortex. Recently, the PCNN has been applied to the various image processing applications, e.g., image segmentation, edge detection, pattern recognition, and so on. The method for the image matching using the PCNN had been proposed as one of the valuable applications of the PCNN. In this method, the Genetic Algorithm is applied to the PCNN parameter learning for the image matching. In this study, we propose the method of the similar image rating using the PCNN. In our method, the Genetic Algorithm based method is applied to the parameter learning of the PCNN. We show the performance of our method by simulations. From the simulation results, we evaluate the efficiency and the general versatility of our parameter learning method.

• Journal of the Korean Society of Clothing and Textiles
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• v.21 no.5
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• pp.880-888
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• 1997

This study was intended to analyze the perception of consumers towards TV commercials of women's clothes and variables influencing the effectiveness of the commercials. The sample consisted of 408 female students attending Ehwa Woman's university and the survey was conducted after the TV commercials were shown to the sample. Frequency, percentage, F-test, logistic regression were used for analysis. Conclusions of the study are as follows, 1. TV commercials were grouped into three image categories, Individuality, Nobility, and Activity. The commercials of the formal clothes were perceived based on nobility factor while the commercials of the casual clothes were perceived based on individuality factor by subjects. 2. Commercial image and the brand image appeared similar in three image factors. And TV commercials were more effective when two images were perceived similar. 3. The expenditures on TV commercial influenced the awareness of commercials, therefore frequent commercial drew more awareness. 4. The models on the commercials were more effective when the image of the commercials and the image of the models were perceived similar by subjects.

• Korean Institute of Interior Design Journal
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• no.7
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• pp.31-37
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• 1996

The purpose of this study is to grasp the characteristics by image type in interior color. This experiment is carried out by the evaluation method of color simulation with the color image processor. And the result of this experimental evaluation is analyzed quantitatively by HAYASI 1 Program. The results of this analysis are as follows : 1) In casual and clear images, the most main colors are GY, PB, Y , and N, Casual image has high chroma and the most arrangement of colors is hue-contrast or contrast -harmony with white. Also the main colors of clear image are in identical or similar harmony with the hues of floor. 2) In romantic, elegant, pretty, and gorgeous images, the most main colors are GY, RP, R , YR and Y and the most arrangement of colors is identical or similar harmony. The romantic image of pastel tone is wholly lighter than the pretty image of bright tone. And elegant image is lower in chroma than romantic images, so generally dark. Also gorgeous image is the vivid tone with high chroma. 3) In chic and modern images, the main colors are the hues of B, PB, high value and low chroma with bright tone. Also, the main colors are in identical or similar harmony with the hues of floor : BG , B, PB and P. 4) In natural and semiclassic images, the main color is the warm color of Y, YR and the most arrangement of colors is identical or similar harmony. Also Semiclassic images is the dull tone with middle value and middle chroma and darker tone than natural image. 5) In dynamic image, the main color is the hue of N, Y, PB and GY and most of color is high chroma. And the most arrangement of colors is value-contrast.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.27 no.3
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• pp.59-65
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• 2004

This paper explores an image segmentation and representation method using Vector Quantization(VQ) on color and texture for content-based image retrieval system. The basic idea is a transformation from the raw pixel data to a small set of image regions which are coherent in color and texture space. These schemes are used for object-based image retrieval. Features for image retrieval are three color features from HSV color model and five texture features from Gray-level co-occurrence matrices. Once the feature extraction scheme is performed in the image, 8-dimensional feature vectors represent each pixel in the image. VQ algorithm is used to cluster each pixel data into groups. A representative feature table based on the dominant groups is obtained and used to retrieve similar images according to object within the image. The proposed method can retrieve similar images even in the case that the objects are translated, scaled, and rotated.

• Journal of information and communication convergence engineering
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• v.19 no.1
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• pp.36-41
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• 2021

Text-based image retrieval is not only cumbersome as it requires the manual input of keywords by the user, but is also limited in the semantic approach of keywords. However, content-based image retrieval enables visual processing by a computer to solve the problems of text retrieval more fundamentally. Vision applications such as extraction and mapping of image characteristics, require the processing of a large amount of data in a mobile environment, rendering efficient power consumption difficult. Hence, an effective image retrieval method on mobile platforms is proposed herein. To provide the visual meaning of keywords to be inserted into images, the efficiency of image retrieval is improved by extracting keywords of exchangeable image file format metadata from images retrieved through a content-based similar image retrieval method and then adding automatic keywords to images captured on mobile devices. Additionally, users can manually add or modify keywords to the image metadata.

• Journal of KIISE:Databases
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• v.35 no.5
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• pp.379-390
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• 2008

Due to the increase of the amount of image data, the demand for searching similar images is continuously increasing. Therefore, many researches about the content-based image retrieval (CBIR) are conducted to search similar images effectively. In CBIR, it uses image contents such as color, shape, and texture for more effective retrieval. However, when we apply CBIR to satellite images which are complex and pose the difficulty in using color information, we can have trouble to get a good retrieval result. Since it is difficult to use color information of satellite images, we need image segmentation to use shape information by separating the shape of an object in a satellite image. However, because satellite images are complex, image segmentation is hard and poor image segmentation results in poor retrieval results. In this paper, we propose a new approach to search similar images without image segmentation for satellite images. To do a similarity search without image segmentation, we define a similarity of an image by considering SIFT keypoint descriptors which doesn't require image segmentation. Experimental results show that the proposed approach more effectively searches similar satellite images which are complex and pose the difficulty in using color information.

• Journal of the Korean Society of Clothing and Textiles
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• v.30 no.4 s.152
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• pp.531-541
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• 2006

The purpose of this study is to examine the semantic structure about the image shown on the product, brand and advertisement and figuring out its features through the correlation among brand images. For the study, nine brands(Guess, Bangbang, ONG, NIX, TBJ, Levi's, OPT, FRJ, Jambangee) as subjects for investigation has been selected and divided into the image of brand(9 brands), product(108 products, 12 pieces for each product) and advertisement(9 points) by the measure of 26 adjective pairs. The survey has been collected on the subject of 540 men and women who live in around Busan city areas and has been taken the statistics. The results on investigating the semantic structures of the product images about jean brands, there are five main factors, such as, individuality, attractiveness, activeness, modernity, hardness & softness. The results on examining the semantic structures of the brand images about jean brands, the factors are attractiveness, activeness, vitality, hardness & softness, fadness. The results on investigating the semantic structure of the advertisement images about jean brands, the factors are attractiveness, individuality, modernity, activeness. The results on the classification of brand images are presented as four groups, the first group is that brand and advertisement image are pretty similar but product image is differential according to brand and the second group, product and advertisement image are similar but brand image is differential. The third group is that product and brand image are similar but advertisement is differential and the fourth group, product, brand and advertisement are similar.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.1
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• pp.11-24
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• 2008

Region-based image retrieval techniques, which divide image into similar regions having similar characteristics and examine similarities among divided regions, were proposed to support an efficient low-dimensional color indexing scheme. However, color image segmentation techniques are required additionally. The problem of segmentation is difficult because of a large variety of color and texture. It is known to be difficult to identify image regions containing the same color-texture pattern in natural scenes. In this paper we propose an automatic color image segmentation algorithm. The colors in each image are first quantized to reduce the number of colors. The gray level of image representing the outline edge of image is constructed in terms of Fisher's multi-class linear discriminant on quantized images. The gray level of image is transformed into a binary edge image. The edge showing the outline of the binary edge image links to the nearest edge if disconnected. Finally, the final segmentation image is obtained by merging similar regions. In this paper we design and implement a region-based image retrieval system using the proposed segmentation. A variety of experiments show that the proposed segmentation scheme provides good segmentation results on a variety of images.

• Journal of the Korea Computer Industry Society
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• v.4 no.4
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• pp.613-624
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• 2003

This paper proposes a design for efficient and accurate retrieval of similar image subsequences using the multi-dimensional time warping distance as similarity evaluation tool in image sequence database after building of two indexing structures implemented with priority queue and suffix tree respectively. Receiving query image sequence, at first step, the proposed method searches the candidate set of similar image subsequences in priory queue index structure. If it can not get satisfied results, it retrieves another candidate set in suffix tree index structure at second step. The using of the low-bound distance function can remove the dissimilar subsequence without false dismissals during similarity evaluating process between query image sequence and stored sequences in two index structures.