• Journal of the Semiconductor & Display Technology
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• v.12 no.4
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• pp.33-38
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• 2013

Image retrieval is one of the most exciting and fastest growing research fields in the area of multimedia technology. As the amount of digital contents continues to grow users are experiencing increasing difficulty in finding specific images in their image libraries. This paper proposes an efficient image descriptor which uses a local color and texture in the non-overlapped block images. To evaluate the performance of the proposed method, we assessed the retrieval efficiency in terms of ANMRR with common image dataset. The experimental trials revealed that the proposed algorithm exhibited a significant improvement in ANMRR, compared to Dominant Color Descriptor and Edge Histogram Descriptor.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.33-40
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• 2023

The mainstay of current image recovery frameworks is Content-Based Image Retrieval (CBIR). The most distinctive retrieval method involves the submission of an image query, after which the system extracts visual characteristics such as shape, color, and texture from the images. Most of the techniques use RGB color space to extract and classify images as it is the default color space of the images when those techniques fail to change the color space of the images. To determine the most effective color space for retrieving images, this research discusses the transformation of RGB to different color spaces, feature extraction, and usage of Convolutional Neural Networks for retrieval.

• International Journal of Control, Automation, and Systems
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• v.1 no.3
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• pp.332-338
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• 2003

Content-based image retrieval is the research of images from database, that are visually similar to given image examples. Gabor functions and Gabor filters are regarded as excellent methods for feature extraction and texture segmentation. However, they have a disadvantage not to perform well in case of a rotated image because of its direction-oriented filter. This paper proposes a method of extracting local texture features from blocks with central interest points detected in an image and a rotation invariant Gabor wavelet filter. We also propose a method of comparing pattern histograms of features classified by VQ (Vector Quantization) among images.

• Journal of the Semiconductor & Display Technology
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• v.14 no.2
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• pp.53-59
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• 2015

Since the amount of digital image continues to grow in usage, users feel increased difficulty in finding specific images from the image collection. This paper proposes a novel image searching scheme that extracts the image feature using combination of Advanced SURF (Speed-Up Robust Feature) and DCD (Dominant Color Descriptor). The key point of this research is to provide a new feature extraction algorithm to improve the existing SURF method with removal of unnecessary feature in image retrieval, which can be adaptable to mobile system and efficiently run on the mobile environments. To evaluate the proposed scheme, we assessed the performance of simulation in term of average precision and F-score on two databases, commonly used in the field of image retrieval. The experimental results revealed that the proposed algorithm exhibited a significant improvement of over 14.4% in retrieval effectiveness, compared to OpenSURF. The main contribution of this paper is that the proposed approach achieves high accuracy and stability by using ASURF and DCD in searching for natural image on mobile platform.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.160-161
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• 2010

In this paper, trademark image retrieval system is proposed by using color information and shape information. We use the image for a color information by dividing into the area and extracting the area color distribution histogram. We use for the shape information by preprocessing of the boundary extraction, the centroid extraction, angular sampling etc. and calculating of the sum of the distance from the centroid to the boundary, the standard deviation, and the rate of long axis to short axis. In particular, centroid by using the angular sampling can extract feature and reduce the processing time. Users can perform searchs using the color and shape information, and also the two methods by mixing can be used by weighting.

• Journal of IKEEE
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• v.7 no.2 s.13
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• pp.210-217
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• 2003

A novel characteristic value extraction method based on mathematical morphology is proposed. Morphological spatial frequency defined by morphological pattern distribution function is introduced and applied to define a new feature called ‘average height.' The average height is used to define a characteristic value which is to be used to generate an index key value for content-based image retrieval. Superiority of the method was proved for various images by experiment. Furthermore the fact that the proposed method does not need threshold to obtain binary image provides its applicability to content-based image retrieval.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.5
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• pp.1676-1689
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• 2014

At present, the Content-Based Image Retrieval (CBIR) system has become a hot research topic in the computer vision field. In the CBIR system, the accurate extractions of low-level features can reduce the gaps between high-level semantics and improve retrieval precision. This paper puts forward a new retrieval method aiming at the problems of high computational complexities and low precision of global feature extraction algorithms. The establishment of the new retrieval method is on the basis of the SIFT and Harris (APISH) algorithm, and the salient region of interest points (SRIP) algorithm to satisfy users' interests in the specific targets of images. In the first place, by using the IPDSH and SRIP algorithms, we tested stable interest points and found salient regions. The interest points in the salient region were named as salient interest points. Secondary, we extracted the pseudo-Zernike moments of the salient interest points' neighborhood as the feature vectors. Finally, we calculated the similarities between query and database images. Finally, We conducted this experiment based on the Caltech-101 database. By studying the experiment, the results have shown that this new retrieval method can decrease the interference of unstable interest points in the regions of non-interests and improve the ratios of accuracy and recall.

• Proceedings of the Korea Multimedia Society Conference
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• 2000.04a
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• pp.328-331
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• 2000

In this paper , we propose a texture feature extraction method of reduce the massive computational time on extracting texture, features of large sized medical such as MRI, CT-scan , and an index structure, called GLTFT, to speed up the retrieval performance. For these, the original image is transformed into a compressed image by Wavelet transform , and textural features such as contrast, energy, entropy, and homogeneity of the compressed image is extracted by using GLCM(Gray Level Co-occurrence Metrix) . The proposed index structure is organized by using the textural features. The processing in compressed domain can give the solution of storage space and the reduction of computational time of feature extracting . And , by GLTFT index structure, image retrieval performance can be expected to be improved by reducing the retrieval range . Our experiment on 270 MRIs as image database shows that shows that such expectation can be got.

• Journal of Information Processing Systems
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• v.13 no.6
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• pp.1628-1639
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• 2017

The combination texture feature extraction approach for texture image retrieval is proposed in this paper. Two kinds of low level texture features were combined in the approach. One of them was extracted from singular value decomposition (SVD) based dual-tree complex wavelet transform (DTCWT) coefficients, and the other one was extracted from multi-scale local binary patterns (LBPs). The fusion features of SVD based multi-directional wavelet features and multi-scale LBP features have short dimensions of feature vector. The comparing experiments are conducted on Brodatz and Vistex datasets. According to the experimental results, the proposed method has a relatively better performance in aspect of retrieval accuracy and time complexity upon the existing methods.

• Journal of Korea Multimedia Society
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• v.6 no.6
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• pp.1008-1014
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• 2003

In this paper, we propose the image retrieval method based on object regions using bidirectional round filter in the wavelet transform domain. A conventional method that includes unnecessary background information reduce retrieval efficiency, because of the extraction of feature vectors from the whole region of subband. On proposed method, it extracts accurate feature vectors and keep certainly retrieval efficiency in case of reduced feature vectors, because of the extraction of feature vectors from the only extracted object region. Furthermore, it improve retrieval efficiency by removing unnecessary background information. Consequently, the retrieval efficiency is improved with 2.5%∼5.5% values, which have a little chances to vary according to characteristics of image.