• Proceedings of the IEEK Conference
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• 2000.11c
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• pp.129-132
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• 2000

In this paper, a multimedia database retrieval system is proposed using MPEG-7 meta data. Multimedia content based retrieval system is implemented with the MPEG-7 meta data extraction and matching technique. MPEG-7 descriptors and descriptor schemes are stored into the database with other meta data. When a query image is given, the descriptors and descriptor schemes of the query image are extracted and compared with the descriptors and descriptor schemes in the database. Finally, images having more similarity are retrieved.

• The Journal of the Korea Contents Association
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• v.4 no.1
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• pp.18-23
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• 2004

In this paper, we propose a new visual feature extraction method for content-based image retrieval(CBIR) based on wavelet transform which has both spatial-frequency characteristic and multi-resolution characteristic. We extract visual features for each frequency band in wavelet transformation and use them to CBIR. The lowest frequency band involves spacial information of original image. We extract L feature vectors using fuzzy homogeneity in the wavelet domain, which consider both the wavelet coefficients and the spacial information of each coefficient. Also, we extract 3 feature vectors wing the energy values of high frequency bands, and store those to image database. As a query, we retrieve the most similar image from image database according to the 10 largest homograms(normalized fuzzy homogeneity vectors) and 3 energy values. Simulation results show that the proposed method has good accuracy in image retrieval using 90 texture images.

• The KIPS Transactions:PartB
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• v.10B no.7
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• pp.823-828
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• 2003

The main objective of this paper is to provide a methodology of feature extraction using shape of image objects for content-based image retrieval. The shape of most real-life objects is irregular, and hence there is no universal approach to quantify the shape of an arbitrary object. In particular. electronic catalogs contain many image objects for their products. In this paper, we perform feature extraction based on individual objects in images rather than on the whole image itself, since our method uses a shape-based approach of objects using RLC lines within an image. Experiments show that shape parameters distinctly represented image objects and provided better classification and discrimination among image objects in an image database compared to Texture.

• Journal of KIISE
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• v.42 no.1
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• pp.33-43
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• 2015

Recently, patent images are growing importance and thus patent image retrieval is a growing area of research. However, most existing patent image retrieval systems use edges extracted in the images, whose performance is affected by the quality of edge detection in the image pre-processing step. To overcome this disadvantage, we propose a SURF-based patent image retrieval method which uses the morphological characteristics of the images. The proposed method detects SURF interest points with directions and computes regional histograms. We apply the proposed method to a patent image database with 2000 binary images and we show the proposed retrieval system achieves excellent results, even when the images have some loss or degradation.

• 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.

• The KIPS Transactions:PartB
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• v.13B no.3 s.106
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• pp.309-314
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• 2006

Recently, multimedia DBMS is appeared to be the core technology of the information society to store, manage and retrieve multimedia data efficiently. In this paper, we propose a new method for content based-retrieval system using wavelet transform, energy value to extract automatically feature vector from image data, and suggest an effective retrieval technique through this method. Wavelet transform is widely used in image compression and digital signal analysis, and its coefficient values reflect image feature very well. The correlation in wavelet domain between query image data and the stored data in database is used to calculate similarity. In order to assess the image retrieval performance, a set of hundreds images are run. The method using standard derivation and mean value used for feature vector extraction are compared with that of our method based on energy value. For the simulation results, our energy value method was more effective than the one using standard derivation and mean value.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.581-584
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• 1999

In order to retrieve the rotated image within database by the content based image retrieval system, the algorithms with rotation robustness is usually applied in the procedure of the feature extraction. In that case, it requires much calculation time for feature extraction and much indexed data for feature indexing. Thus. in this paper. we propose the rotation robust algorithm using the block variance of the projected vector. The algorithm does not require additional calculation for feature extraction and is executed within query time by comparing the extracted data. Proposed method can be processed through database including various size of images with shape information and executed with fast response time in implementation.

• The KIPS Transactions:PartB
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• v.10B no.6
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• pp.681-686
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• 2003

In recent years automatic image indexing and retrieval have been increasingly studied. However, content-based retrieval techniques for general images are still inadequate for many purposes. The novelty and originality of this thesis are the definition and use of a spatial information model as a contribution to the accuracy and efficiency of image search. In addition, the model is applied to represent color and shape image contents as a vector using the method of image features extraction, which was inspired by the previous work on the study of human visual perception. The indexing scheme using the color, shape and spatial model shows the potential of being applied with the well-developed algorithms of features extraction and image search, like ranking operations. To conclude, user can retrieved more similar images with high precision and fast speed using the proposed system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3790-3803
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• 2018

Content-based image retrieval is an approach used to query images based on their semantics. Semantic based retrieval has its application in all fields including medicine, space, computing etc. Semantically generated binary hash codes can improve content-based image retrieval. These semantic labels / binary hash codes can be generated from unlabeled data using convolutional autoencoders. Proposed approach uses semi-supervised deep hashing with semantic learning and binary code generation by minimizing the objective function. Convolutional autoencoders are basis to extract semantic features due to its property of image generation from low level semantic representations. These representations of images are more effective than simple feature extraction and can preserve better semantic information. Proposed activation and loss functions helped to minimize classification error and produce better hash codes. Most widely used datasets have been used for verification of this approach that outperforms the existing methods.

• Journal of KIISE:Computer Systems and Theory
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• v.28 no.10
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• pp.507-512
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• 2001

Recently, there has been studied on feature extraction method for efficient content-based image retrieval. Especially, many researchers have been studying on extracting from color information, because of its advantages. This paper proposes a feature and its extraction method based on color information in an image. The proposed method is computed from the image segmented into two parts: the complex part and the plan part. Our experiments show that the performance of the proposed method is better as compared with the original color correlogram method.