• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.6
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• pp.27-37
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• 2008

Applications of 3D data increase with advancement of multimedia technique and contents, and it is necessary to manage and to retrieve for 3D data efficiently. In this paper, we propose a new method using the sliced shape which extracts efficiently a feature description for shape-based retrieval of 3D models. Since the feature descriptor of 3D model should be invariant to translation, rotation and scale for its model, normalization of models requires for 3D model retrieval system. This paper uses principal component analysis(PCA) method in order to normalize all the models. The proposed algorithm finds a direction of each axis by the PCA and creates orthogonal n planes in each axis. These planes are orthogonalized with each axis, and are used to extract sliced shape image. Sliced shape image is the 2D plane created by intersecting at between 3D model and these planes. The proposed feature descriptor is a distribution of Euclidean distances from center point of sliced shape image to its outline. A performed evaluation is used for average of the normalize modified retrieval rank(ANMRR) with a standard evaluation from MPEG-7. In our experimental results, we demonstrate that the proposed method is an efficient 3D model retrieval.

• Science of Emotion and Sensibility
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• v.7 no.4
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• pp.57-65
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• 2004

In this paper, we present a hybrid approach which incorporates color, texture and shape in content-based image retrieval. Colors in each image are clustered into a small number of representative colors. The feature descriptor consists of the representative colors and their percentages in the image. A similarity measure similar to the cumulative color histogram distance measure is defined for this descriptor. The co-occurrence matrix as a statistical method is used for texture analysis. An optimal set of five statistical functions are extracted from the co-occurrence matrix of each image, in order to render the feature vector for eachimage maximally informative. The edge information captured within edge histograms is extracted after a pre-processing phase that performs color transformation, quantization, and filtering. The features where thus extracted and stored within feature vectors and were later compared with an intersection-based method. The content-based retrieval system is tested to be effective in terms of retrieval and scalability through experimental results and precision-recall analysis.

• Journal of Digital Convergence
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• v.12 no.6
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• pp.237-243
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• 2014

In this paper, we propose a mobile image retrieval system which utilizes the mobile device's sensor information and enables running in a variety of the environments, and implement the system on Android platform. The proposed system deals with a new image descriptor using combination of the visual feature with EXIF attributes in the target of JPEG image, and image matching algorithm which is optimized to the mobile environments. Experiments are performed on the Android platform, and the experimental results revealed that the proposed algorithm exhibits a significant improved results with large image database.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3312-3327
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• 2020

Plant diseases are a significant yield and quality constraint for farmers around the world due to their severe impact on agricultural productivity. Such losses can have a substantial impact on the economy which causes a reduction in farmer's income and higher prices for consumers. Further, it may also result in a severe shortage of food ensuing violent hunger and starvation, especially, in less-developed countries where access to disease prevention methods is limited. This research presents an investigation of Directional Local Quinary Patterns (DLQP) as a feature descriptor for plants leaf disease detection and Support Vector Machine (SVM) as a classifier. The DLQP as a feature descriptor is specifically the first time being used for disease detection in horticulture. DLQP provides directional edge information attending the reference pixel with its neighboring pixel value by involving computation of their grey-level difference based on quinary value (-2, -1, 0, 1, 2) in 0°, 45°, 90°, and 135° directions of selected window of plant leaf image. To assess the robustness of DLQP as a texture descriptor we used a research-oriented Plant Village dataset of Tomato plant (3,900 leaf images) comprising of 6 diseased classes, Potato plant (1,526 leaf images) and Apple plant (2,600 leaf images) comprising of 3 diseased classes. The accuracies of 95.6%, 96.2% and 97.8% for the above-mentioned crops, respectively, were achieved which are higher in comparison with classification on the same dataset using other standard feature descriptors like Local Binary Pattern (LBP) and Local Ternary Patterns (LTP). Further, the effectiveness of the proposed method is proven by comparing it with existing algorithms for plant disease phenotyping.

• International Journal of Internet, Broadcasting and Communication
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• v.10 no.4
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• pp.50-64
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• 2018

This paper describes a visual object detection approach utilizing ensemble based machine learning. Object detection methods employing 1D features have the benefit of fast calculation speed. However, for real image with complex background, detection accuracy and performance are degraded. In this paper, we propose an ensemble learning algorithm that combines a 1D feature classifier and 2D DNF (Disjunctive Normal Form) classifier to improve the object detection performance in a single input image. Also, to improve the computing efficiency and accuracy, we propose a feature selecting method to reduce the computing time and ensemble algorithm by combining the 1D features and 2D DNF features. In the verification experiments, we selected the Haar-like feature as the 1D image descriptor, and demonstrated the performance of the algorithm on a few datasets such as face and vehicle.

• Journal of Advanced Marine Engineering and Technology
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• v.38 no.9
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• pp.1112-1118
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• 2014

In this paper, we propose a local descriptor and a similarity measure that is robust to radiometic variations. The proposed local descriptor is made up Haar wavelet filter and it can contain frequency informations about the feature point and its surrounding pixels in fixed region, and it is able to describe feature point clearly under ununiform illumination condition. And a proposed similarity measure is combined with conventional entropy-based similarity and another similarities that is generated by local descriptor. It can reflect similarities between image regions accurately under radiometic illumination variations. We validate with experimental results on some images and we confirm that the proposed algorithm is more superior than conventional algorithms.

• Journal of the HCI Society of Korea
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• v.2 no.2
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• pp.35-43
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• 2007

This paper presents a system for image matching and recognition based on image feature detection and description techniques from artificial satellite photographs. We propose some kind of parameters from the varied environmental elements happen by image handling process. The essential point of this experiment is analyzes that affects match rate and recognition accuracy when to change of state of each parameter. The proposed system is basically inspired by Lowe's SIFT(Scale-Invariant Transform Feature) algorithm. The descriptors extracted from local affine invariant regions are saved into database, which are defined by k-means performed on the 128-dimensional descriptor vectors on an artificial satellite photographs from Google earth. And then, a label is attached to each cluster of the feature database and acts as guidance for an appeared building's information in the scene from camera. This experiment shows the various parameters and compares the affected results by changing parameters for the process of image matching and recognition. Finally, the implementation and the experimental results for several requests are shown.

• Journal of Korea Multimedia Society
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• v.16 no.4
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• pp.483-492
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• 2013

SURF is an algorithm which extracts feature points and generates their descriptors from input images, and it is being used for many applications such as object recognition, tracking, and constructing panorama pictures. Although SURF is known to be robust to changes of scale, rotation, and view points, it is hard to implement it in real time due to its complex and repetitive computations. Using 3.3 GHz Pentium, in our experiment, it takes 240ms to extract feature points and create descriptors in a VGA image containing about 1,000 feature points, which means that software implementation cannot meet the real time requirement, especially in embedded systems. In this paper, we present a hardware architecture that can compute the SURF algorithm very fast while consuming minimum hardware resources. Two key concepts of our architecture are parallelism (for repetitive computations) and efficient line memory usage (obtained by analyzing memory access patterns). As a result of FPGA synthesis using Xilinx Virtex5LX330, it occupies 101,348 LUTs and 1,367 KB on-chip memory, giving performance of 30 frames per second at 100 MHz clock.

• The KIPS Transactions:PartB
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• v.10B no.2
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• pp.179-188
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• 2003

In content-based image retrieval, the query is an image itself and the retrieval process is the process that seeking the similar images to the given query image. In this way of retrieval, the user has to know the basic physical features of target images that he wants to retrieve. But it has some restriction because to retrieve the target image he has to know the basic physical feature space such as color, texture, shape and spatial relationship. In this paper, we propose an emotion-based retrieval system. It uses the emotion that color images have. It is different from past emotion-based image retrieval in point of view that it uses relevance feedback to estimate the users intend and it is easily combined with past content-based image retrieval system. To test the performance of our proposed system, we use MPEG-7 color descriptor and emotion language such as "warm", "clean", "bright" and "delight" We test about 1500 wallpaper images and get successful result.lpaper images and get successful result.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.6
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• pp.1214-1218
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• 2006

This paper presents method to speed up processing time in construction of panoramic images. The method based on invariant feature uses image down-scaling and image edge information. Reducing image size and applying feature descriptor to image portions superimposed with edge causes to reduce the number of features and to improve processing speed. In the experiments, it was shown that the proposed method was 3.26$\sim$13.87% shorter in processing time than the exiting method fer 24-bit color images of 640$\times$480 size.