• Journal of Korean Institute of Industrial Engineers
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• v.26 no.4
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• pp.363-375
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• 2000

In this paper we propose a content-based image retrieval method that can search large image databases efficiently by color, texture, and shape content. Quantized RGB histograms and the dominant triple (hue, saturation, and value), which are extracted from quantized HSV joint histogram in the local image region, are used for representing global/local color information in the image. Entropy and maximum entry from co-occurrence matrices are used for texture information and edge angle histogram is used for representing shape information. Relevance feedback approach, which has coupled proposed features, is used for obtaining better retrieval accuracy. Simulation results illustrate the above method provides 77.5 percent precision rate without relevance feedback and increased precision rate using relevance feedback for overall queries. We also present a new indexing method that supports fast retrieval in large image databases. Tree structures constructed by k-means algorithm, along with the idea of triangle inequality, eliminate candidate images for similarity calculation between query image and each database image. We find that the proposed method reduces calculation up to average 92.9 percent of the images from direct comparison.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.2
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• pp.117-128
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• 2001

In order to achieve optimal retrieval, i.e., to find the best match to a query according to a certain similarity measure, the exhaustive search should be performed literally for all the images in a database. However, the straightforward exhaustive search algorithm is computationally expensive in large image databases. To reduce its heavy computational cost, this paper presents a fast exhaustive multi-resolution search algorithm based on image database clustering. Firstly, the proposed algorithm partitions the whole image data set into a pre-defined number of clusters having similar feature contents. Next, for a given query, it checks the lower bound of distances in each cluster, eliminating disqualified clusters. Then, it only examines the candidates in the remaining clusters. To alleviate unnecessary feature matching operations in the search procedure, the distance inequality property is employed based on a multi-resolution data structure. The proposed algorithm realizes a fast exhaustive multi-resolution search for either the best match or multiple best matches to the query. Using luminance histograms as a feature, we prove that the proposed algorithm guarantees optimal retrieval with high searching speed.

• Journal of Korea Spatial Information System Society
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• v.11 no.1
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• pp.25-34
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• 2009

As the trend of the Web is changing toward 'Web 2.0', OpenAPIs, Web 2.0's core technology, are used in many web sites. In the past, services in websites are used in its own, but recently it is possible to use services in other websites by using OpenAPI. In u-GIS many vendors also can provide combined service by using OpenAPI. There are already lots of OpenAPIs and the numer of OpenAPI increases very fast. So it is difficult to find a service that we want to use, and also difficult to find services for mashup. In this paper, we developed retrieval methods for OpenAPIs and mashup capable services based on similarity. First we define the integrated service information model to cover various protocols of OpenAPI, then developed a retrieval methods based on it. By implementing system according these methods by using relational database and JSP, we prove that the system can provide an ranked result sets based on similarity, OpenAPI's integration retrieval results and mashup capable service retrieval results.

• The Transactions of the Korea Information Processing Society
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• v.3 no.4
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• pp.733-744
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• 1996

Content-based image retrieval(CBIR) is an image data retrieval methodology using characteristic values of image data those are generated by system automatically without any caption or text information. In this paper, we propose a content-based image data retrieval method using shape and color features of image data as characteristic values. For this, we present some image processing techniques used for feature extraction and indexing techniques based on trie and R tree for fast image data retrieval. In our approach, image query result is more reliable because both shape and color features are considered. Also, we how an image database which implemented according to our approaches and sample retrieval results which are selected by our system from 200 sample images, and an analysis about the result by considering the effect of characteristic values of shape and color.

• Journal of the Korea Computer Industry Society
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• v.4 no.12
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• pp.901-910
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• 2003

Generally, most commercial retrieval engines adopt boolean query as user's query type. Although boolean query is useful to retrieval engines that need fast retrieval, it is not easy for user to express his demands with boolean operators. So, many researches have been studied for decades about information retrieval systems using natural language query that is convenient for user. To retrieve documents that are suitable for user's demands, they have to express their demands correctly, So, this thesis proposes interactive query process agent using natural language. This agent expresses demands concrete through gradual interaction with user, When users input a natural language Query, this agent analyzes the query and generates boolean query by selecting proper keyword and feedbacks the state of the keyword selected. If the keyword is a synonymy or a polysemy, the agent expands or limits the keyword through interaction with user. It makes user express demands more concrete and improve system performance. So, this agent can improve the precision of Information Retrieval.

• Journal of Korea Multimedia Society
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• v.10 no.11
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• pp.1398-1406
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• 2007

Providing natural and efficient access to the fast growing multimedia information is a critical aspect for content-based information system. Query by humming system allows the user to find a song by humming part of the tune form music database. Conventional music information retrieval systems use a high precision pitch extraction method. However, it is very difficult to extract true pitch perfectly. So, In this paper, we propose to use YIN parameter with applying the reliability to reduce the pitch extraction errors. And we describes developed music information retrieval method based on a query by humming system which uses reliable feature extraction. Developed system is based on a continuous dynamic programming algorithm with features including pitch, duration and energy along with their confidence measures. The experiment showed that the proposed method could reduce the errors of the top-10 7.2% and the top-1 9.1% compared with the cepsturm based multiple pitch candidate. The overall retrieval system achieved 92.8% correct retrieval in the top-10 rank list on a database of 155 songs.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.4
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• pp.273-280
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• 2013

This paper presents novel OCS-LBP (Oriented Center Symmetric Local Binary Patterns) based on orientation of pixel gradient and image retrieval system based on BoF (Bag-of-Feature) and random forest classifier. Feature vectors extracted from training data are clustered into code book and each feature is transformed new BoF feature using code book. BoF features are applied to random forest for training and random forest having N classes is constructed by combining several decision trees. For testing, the same OCS-LBP feature is extracted from a query image and BoF is applied to trained random forest classifier. In contrast to conventional retrieval system, query image selects similar K-nearest neighbor (K-NN) classes after random forest is performed. Then, Top K similar images are retrieved from database images that are only labeled K-NN classes. Compared with other retrieval algorithms, the proposed method shows both fast processing time and improved retrieval performance.

• Journal of Korea Multimedia Society
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• v.5 no.4
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• pp.415-425
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• 2002

In this paper, we present a content-based image retrieval method which is based on the feature extraction in the wavelet transform domain. In order to overcome the drawbacks of the feature vector making up methods which use the global wavelet coefficients in subbands, we utilize the energy value of wavelet coefficients, and the shape-based retrieval of objects is processed by moment which is invariant in translation, scaling, rotation of the objects The proposed methods reduce feature vector size, and make progress performance of classification retrieval which provides fast retrievals times. To offer the abilities of region-based image retrieval, we discussed the image segmentation method which can reduce the effect of an irregular light sources. The image segmentation method uses a region-merging, and candidate regions which are merged were selected by the energy values of high frequency bands in discrete wavelet transform. The region-based image retrieval is executed by using the segmented region information, and the images are retrieved by a color, texture, shape feature vector.

• Journal of the Optical Society of Korea
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• v.18 no.3
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• pp.217-224
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• 2014

A major advantage of wavefront reconstruction based on a series of diffracted intensity images using only single-beam illumination is the simplicity of setup. Here we propose a fast-converging algorithm for wavefront calculation using single-beam illumination. The captured intensity images are resampled to a series of intensity images, ranging from highest to lowest resampling; each resampled image has half the number of pixels as the previous one. Phase calculation at a lower resolution is used as the initial solution phase at a higher resolution. This corresponds to separately calculating the phase for the lower- and higher-frequency components. Iterations on the low-frequency components do not need to be performed on the higher-frequency components, thus making the convergence of the phase retrieval faster than with the conventional method. The principle is verified by both simulation and optical experiments.

• Proceedings of the Korea Contents Association Conference
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• 2003.05a
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• pp.416-420
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• 2003

In this paper, we implemented a dual-resolution image database system for the fast naked-eye retrieval using interpolation. This system can solve two conventional problems : a blocking noise at zoom-out image in single high resolution method and a big storage to store in simple dual-resolution image database system. The proposed method makes a subsampled image by subsampling a original image, and then a interpolated image of it using interpolation. After that, a hybrid dual-resolution image database is composed based on the differential image between the interpolated image and the original image. Experimental results of simulating through 60 sample images shows that the proposed method is 0.011 second faster than simple high-resolution method in the retrieval time - one is 0.003 second, the other is 0.014 second, respectively. Also, that improves 14.7% more than simple dual-resolution method in the stored size - one is 19,821 byte, the other is 16,910 byte, respectively.