• Journal of Korea Multimedia Society
• /
• v.5 no.4
• /
• pp.415-425
• /
• 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 Korea Multimedia Society
• /
• v.17 no.10
• /
• pp.1141-1149
• /
• 2014

Recently, various methods to effectively eliminate the noise are researched in image processing techniques. However, the conventional noise filtering techniques, which remove most of the noise, are less efficient for remained noise detection after filtering due to exploiting no face feature information. In this paper, we proposed a feature vector generation technique in the mouth region by distinguishing and revising the remained noise through gradient correction, when the outline is extracted after performing noise filtering.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.34 no.8C
• /
• pp.772-781
• /
• 2009

This paper proposes a novel algorithm for the efficient classification and retrieval of medical images. After color and edge features are extracted from medical images, these two feature vectors are then applied to a multi-class Support Vector Machine, to give membership vectors. Thereafter, the two membership vectors are combined into an ensemble feature vector. Also, to reduce the search time, Correlated Categories Vector is proposed for similarity matching. The experimental results show that the proposed system improves the retrieval performance when compared to other methods.

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.485-487
• /
• 2003

Independent Component Analysis (ICA) is used to generate ICA filter for computing feature vector for image window. Filters that have high discrimination power are selected to classify image from these ICA filters. Proposed classification algorithm is based on probability distribution of feature vector.

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

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.12 no.1
• /
• pp.13-21
• /
• 2019

The content based on image retrieval makes use of features of information within image such as color, texture and share for Retrieval data. we present a novel approach for improving retrieval accuracy based on DCT Filter-Bank. First, we perform DCT on a given image, and generate a Filter-Bank using the DCT coefficients for each color channel. In this step, DC and the limited number of AC coefficients are used. Next, a feature vector is obtained from the histogram of the quantized DC coefficients. Then, AC coefficients in the Filter-Bank are separated into three main groups indicating horizontal, vertical, and diagonal edge directions, respectively, according to their spatial-frequency properties. Each directional group creates its histogram after employing Otsu binarization technique. Finally, we project each histogram on the horizontal and vertical axes, and generate a feature vector for each group. The computed DC and AC feature vectors bins are concatenated, and it is used in the similarity checking procedure. We experimented using 1,000 databases, and as a result, this approach outperformed the old retrieval method which used color information.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.31 no.6_2
• /
• pp.585-592
• /
• 2013

CBIR is an effective tool to search and extract image contents in a large remote sensing image database queried by an operator or end user. However, as imaging principles are different by sensors, their visual representation thus varies among image modality type. Considering images of various modalities archived in the database, image modality difference has to be tackled for the successful CBIR implementation. However, this topic has been seldom dealt with and thus still poses a practical challenge. This study suggests a cross modality CBIR (termed as the CM-CBIR) method that transforms given query feature vector by a supervised procedure in order to link between modalities. This procedure leverages the skill of analyst in training steps after which the transformed query vector is created for the use of searching in target images with different modalities. Current initial results show the potential of the proposed CM-CBIR method by delivering the image content of interest from different modality images. Despite its retrieval capability is outperformed by that of same modality CBIR (abbreviated as the SM-CBIR), the lack of retrieval performance can be compensated by employing the user's relevancy feedback, a conventional technique for retrieval enhancement.

• Journal of Broadcast Engineering
• /
• v.20 no.5
• /
• pp.728-737
• /
• 2015

Stitching algorithm obtain a descriptor of the feature points extracted from multiple images, and create a single image through the matching process between the each of the feature points. In this paper, a feature extraction and matching techniques for the creation of a high-speed panorama using video input is proposed. Features from Accelerated Segment Test(FAST) is used for the feature extraction at high speed. A new feature point matching process, different from the conventional method is proposed. In the matching process, by tracking region containing the feature point through the Mean shift vector required for matching is obtained. Obtained vector is used to match the extracted feature points. In order to remove the outlier, the RANdom Sample Consensus(RANSAC) method is used. By obtaining a homography transformation matrix of the two input images, a single panoramic image is generated. Through experimental results, we show that the proposed algorithm improve of speed panoramic image generation compared to than the existing method.

• Journal of KIISE:Databases
• /
• v.29 no.5
• /
• pp.358-366
• /
• 2002

Feature-based similarity retrieval become an important research issue in image database systems. The features of image data are useful to discrimination of images. In this paper, we propose the high speed k-Nearest Neighbor search algorithm based on Self-Organizing Maps. Self-Organizing Maps(SOM) provides a mapping from high dimensional feature vectors onto a two-dimensional space and generates a topological feature map. A topological feature map preserves the mutual relations (similarities) in feature spaces of input data, and clusters mutually similar feature vectors in a neighboring nodes. Therefore each node of the topological feature map holds a node vector and similar images that is closest to each node vector. We implemented a k-NN search for similar image classification as to (1) access to topological feature map, and (2) apply to pruning strategy of high speed search. We experiment on the performance of our algorithm using color feature vectors extracted from images. Promising results have been obtained in experiments.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.29B no.4
• /
• pp.48-57
• /
• 1992

This paper addresses the estimation of the shift vector from aerial image sequences. The conventional feature-based and area-based matching methods are simulated for determining the suitable image registration scheme. Computer simulations show that the feature-based matching schemes based on the co-occurrence matrix, autoregressive model, and edge information do not give a reliable matching for aerial image sequences which do not have a suitable statistical model or significant features. In area-based matching methods we try various similarity functions for a matching measure and discuss the factors determining the matching accuracy. To reduce the estimation error of the shift vector we propose the reference window selection scheme. We also discuss the performance of the proposed algorithm based on the simulation results.