• IEIE Transactions on Smart Processing and Computing
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• v.6 no.4
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• pp.229-236
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• 2017

This paper presents an efficient object-based color image-retrieval algorithm that is suitable for the classification and retrieval of images from small to mid-scale datasets, such as images in PCs, tablets, phones, and cameras. The proposed method first finds salient regions by using regional feature vectors, and also finds several dominant colors in each region. Then, each salient region is partitioned into small sub-blocks, which are assigned 1 or 0 with respect to the number of pixels corresponding to a dominant color in the sub-block. This gives a binary map for the dominant color, and this process is repeated for the predefined number of dominant colors. Finally, we have several binary maps, each of which corresponds to a dominant color in a salient region. Hence, the binary maps represent the spatial distribution of the dominant colors in the salient region, and the union (OR operation) of the maps can describe the approximate shapes of salient objects. Also proposed in this paper is a matching method that uses these binary maps and which needs very few computations, because most operations are binary. Experiments on widely used color image databases show that the proposed method performs better than state-of-the-art and previous color-based methods.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.8
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• pp.837-841
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• 2010

This paper proposes an effective similarity measure for content-based image retrieval using MPEG-7 DCD. The proposed method can measure the similarity of images with the percentage of dominant colors extracted from images. As the result of experiments, we achieved a significant improvement of 18.92% with global DCD and 47.22% with local DCD in ANMRR than the result by QHDM. This result shows that the proposed method is an effective similarity measure for content-based image retrieval. Especially, our method is useful for region-based image retrieval.

• Journal of Internet Computing and Services
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• v.10 no.6
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• pp.113-121
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• 2009

In order to retrieve images with different emotions in regions of the images, this paper proposes the image retrieval system using emotion sketch. The proposed retrieval system divides an image into $17{\times}17$ sub-regions and extracts emotion features in each sub-region. In order to extract the emotion features, this paper uses emotion colors on 160 emotion words from H. Nagumo's color scheme imaging chart. We calculate a histogram of each sub-region and consider one emotion word having the maximal value as a representative emotion word of the sub-region. The system demonstrates the effectiveness of the proposed emotion sketch and our experimental results show that the system successfully retrieves on the Corel image database.

• The KIPS Transactions:PartB
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• v.9B no.4
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• pp.501-508
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• 2002

In this paper, we propose a content-based image retrieval using the color ratio and moment of object region. We acquire an optimal spatial information by the region splitting that utilizes horizontal-vertical projection and dominant color. It is based on hypothesis that an object locates in the center of image. We use color ratio and moment as feature informations. Those are extracted from the splitted regions and have the invariant property for various transformation, and besides, similarity measure utilizes a modified histogram intersection to acquire correlation information between bins in a color histogram. In experimental results, the proposed method shows more flexible and efficient performance than existing methods based on region splitting.

• Proceedings of the IEEK Conference
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• 2002.06c
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• pp.207-210
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• 2002

In this paper, content-based retrieval for region of interest(ROI) has been described, using maximum bin color. From a given query image, the object of interest is selected by a user. Using maximum bin color of the selected object, candidate regions are extracted from database images. The final regions of interest are determined by comparing the normalized histograms of the selected object and each candidate region.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.1 s.307
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• pp.27-34
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• 2006

In this paper, we propose a combined region-based image retrieval system using EHD(Edge Histogram Descriptor) and CLD(Color Layout Descriptor) of MPEG-7 descriptors. The combined descriptor can efficiently describe edge and color features in terms of sub-image regions. That is, the basic unit for the selection of the region-of-interest (ROI) in the image is the sub-image block of the EHD, which corresponds to 16 (i.e., $4{\times}4)$ non-overlapping image blocks in the image space. This implies that, to have a one-to-one region correspondence between ELE and CLD, we need to take an $8{\times}8$ inverse DCT (IDCT) for the CLD. Experimental results show that the proposed retrieval scheme can be used for image retrieval with the ROI based image retrieval for MPEG-7 indexed images.

• 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.

• Journal of the Korea Society of Computer and Information
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• v.11 no.4 s.42
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• pp.1-7
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• 2006

Recently, studies of relevance feedback to increase the performance of image retrieval has been activated. In this Paper a new region weighting method in region based image retrieval with relevance feedback is proposed to reduce the semantic gap between the low level feature representation and the high level concept in a given query image. The new weighting method determines the importance of regions according to the spatial locations of regions in an image. Experimental results demonstrate that the retrieval quality of our method is about 18% in recall better than that of area percentage approach. and about 11% in recall better than that of region frequency weighted by inverse image frequency approach and the retrieval time of our method is a tenth of that of region frequency approach.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.364-366
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• 2005

From the 90's, the image information retrieval methods have been on progress. As good examples of the methods, Conventional histogram method and merged-color histogram method were introduced. They could get good result in image retrieval. However, Conventional histogram method has disadvantages if the histogram is shifted as a result of intensity change. Merged-color histogram, also, causes more process so, it needs more time to retrieve images. In this paper, we propose an improved new method using Adaptive Color Histogram Bin Matching(AHB) in image retrieval. The proposed method has been tested and verified through a number of simulations using hundreds of images in a database. The simulation results have Quickly yielded the highly accurate candidate images in comparison to other retrieval methods. We show that AHB's can give superior results to color histograms for image retrieval.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.8B
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• pp.1391-1399
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• 2000

In this paper, we discussed the region-based image retrieval method using image segmentation. We proposed a segmentation method which can reduce the effect of a 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 content-based image retrieval is executed by using the segmented region information, and the images are retrieved by a color, texture, shape feature vector. The similarity measure between regions is processed by the Euclidean distance of the feature vectors. The simulation results shows that the proposed method is reasonable.