• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1334-1337
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• 2004

This paper presents a novel method to speed up neural network (NN) based face detection systems. NN-based face detection can be viewed as a classification and search problem. The proposed method formulates the search problem as an integer nonlinear optimization problem (INLP) and expands the basic particle swarm optimization (PSO) to solve it. PSO works with a population of particles, each representing a subwindow in an input image. The subwindows are evaluated by how well they match a NN-based face filter. A face is indicated when the filter response of the best particle is above a given threshold. To achieve better performance, the influence of PSO parameter settings on the search performance was investigated. Experiments show that with fine-adjusted parameters, the proposed method leads to a speedup of 94 on 320${\times}$240 images compared to the traditional exhaustive search method.

• Journal of Korea Multimedia Society
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• v.14 no.10
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• pp.1275-1286
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• 2011

This paper proposes a high speed systolic array architecture for full search block matching algorithm (FBMA). The pixels of the search area for a reference block are input only one time to find the matched candidate block and reused to compute the sum of absolute difference (SAD) for the adjacent candidate blocks. Each row of designed 2-dimensional systolic array compares the reference block with the adjacent blocks of the same row in search area. The lower rows of the designed array get the pixels from the upper row and compute the SAD with reusing the overlapped pixels of the candidate blocks within same column of the search area. This designed array has no data broadcasting and global paths. The comparison with existing architectures shows that this array is superior in terms of throughput through it requires a little more hardware.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1434-1437
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• 1987

We present a very simple algorithm for reducing the encoding (codebook search) complexity of vector quantization (VQ), exploiting some features of a vector currently being encoded. A proposed VQ of 16 (=$4{\times}4$) vector dimension and 256 codewords shows a slight performance degradation of about 0.1-0.9 dB, however, with only 16 or 32 among 256 codeword searches, i.e., with just 1/16 or 1/8 search complexity compared to a full-search VQ. And the proposed VQ scheme is also compared to and shown to be a bit superior to tree-search VQ with regard to their SNR performance and memory requirement.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.75-100
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• 2022

Untact mobile commerce shows a rapid growth due to the prolonged COVID-19 pandemic. And companies have a lot of tough competition in this trend. However, the detail pages of products which play an important role in purchase decision have been provided mostly for consumers in a form of stereotyped information composition. This study has found that the form of (image-centered vs. text-centered) information composition of detailed descriptions of products in the detail pages of mobile products has an effect on product attitude and purchase intention as consumers' information appeal methods vary depending on product types (search goods vs. experience goods). That is, search goods whose information search is easy and whose quality is predictable could be found that product attitude and purchase intention have a more positive effect on the form of image-centered information composition. And experience goods whose quality is unpredictable could be found that product attitude and purchase intention have a more positive effect on the form of text-centered information composition. And effects of congruence between product types based on Higgins' regulatory focus theory and the form of information composition have found to vary depending on consumers' chronic regulatory focus. Promotion focus seeking consumers showed effects of congruence between product types and the form of information composition and prevention focus seeking consumers did not show effects of congruence between them. That is, promotion focus seeking consumers have found to have more positive product attitude and purchase intention in the form of image-centered information composition of experience goods and text-centered information composition of search goods. And prevention focus seeking consumers have found to be unable to have an effect on product attitude and purchase intention even though the form of image or text-centered information composition of search and experience goods is presented. The study implies that the form of information composition should be designed, produced, and provided for consumers by considering product types and consumer propensity when designing it in the detail pages of mobile products.

• Proceedings of the Korea Contents Association Conference
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• 2005.05a
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• pp.362-368
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• 2005

This paper is related to the fast block matching algorithm, especially a method for expanding the search pattern using the second local matching point in the adaptive hexagonal search. To reduce the local minima problem in fast motion estimation, the proposed method expands the search pattern by adding new searching points selected by using the second local matching point to conventional search pattern formed by the first local matching point in the adaptive hexagonal search. According to estimating the motion vector by applying block matching algorithm based on hexagonal search to the expanded search pattern, the proposed method can effectively carry out fast motion estimation to improve the performance in terms of compensated image quality.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.6
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• pp.455-461
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• 2002

Object-based image retrieval method is addressed. A new image segmentation algorithm and image comparing method between segmented objects are proposed. For image segmentation, color and texture features are extracted from each pixel in the image. These features we used as inputs into VQ (Vector Quantization) clustering method, which yields homogeneous objects in terns of color and texture. In this procedure, colors are quantized into a few dominant colors for simple representation and efficient retrieval. In retrieval case, two comparing schemes are proposed. Comparing between one query object and multi objects of a database image and comparing between multi query objects and multi objects of a database image are proposed. For fast retrieval, dominant object colors are key-indexed into database.

• Journal of information and communication convergence engineering
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• v.19 no.1
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• pp.36-41
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• 2021

Text-based image retrieval is not only cumbersome as it requires the manual input of keywords by the user, but is also limited in the semantic approach of keywords. However, content-based image retrieval enables visual processing by a computer to solve the problems of text retrieval more fundamentally. Vision applications such as extraction and mapping of image characteristics, require the processing of a large amount of data in a mobile environment, rendering efficient power consumption difficult. Hence, an effective image retrieval method on mobile platforms is proposed herein. To provide the visual meaning of keywords to be inserted into images, the efficiency of image retrieval is improved by extracting keywords of exchangeable image file format metadata from images retrieved through a content-based similar image retrieval method and then adding automatic keywords to images captured on mobile devices. Additionally, users can manually add or modify keywords to the image metadata.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.677-680
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• 2003

Perceptual landmarks are an effective solution for a mobile robot realizing steady and reliable long distance navigation. But the prerequisite is those landmarks must be detected and recognized robustly at a higher speed under various lighting conditions. This made image processing more complicated so that its speed and reliability can not be both satisfied at the same time. Color based target detection technique can separate target color regions from non-target color regions in an image with a faster speed, and better results were obtained only under good lighting conditions. Moreover, in the case that there are other things with a target color, we have to consider other target features to tell apart the target from them. Such thing always happens when we detect a target with its single character. On the other hand, we can generally search for only one target for each time so that we can not make use of landmarks efficiently, especially when we want to make more landmarks work together. In this paper, by making use of the moment invariants of each landmark, we can not only search specified target from separated color region but also find multi-target at the same time if necessary. This made the finite landmarks carry on more functions. Because moment invariants were easily used with some low level image processing techniques, such as color based target detection and gradient runs based target detection etc, and moment invariants are more reliable features of each target, the ratio of target detection were improved. Some necessary experiments were carried on to verify its robustness and efficiency of this method.

• Journal of IKEEE
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• v.14 no.4
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• pp.339-345
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• 2010

So far, many researches have been done to index high-dimensional feature values for fast content-based image retrieval. Still, many existing indexing schemes are suffering from performance degradation due to the curse of dimensionality problem. As an alternative, heuristic algorithms have been proposed to calculate the result with 'high probability' at the cost of accuracy. In this paper, we propose a new extendible hash-based indexing scheme for high-dimensional feature values. Our indexing scheme provides several advantages compared to the traditional high-dimensional index structures in terms of search performance and accuracy preservation. Through extensive experiments, we show that our proposed indexing scheme achieves outstanding performance.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.40-48
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• 2021

Content-Based Image Retrieval (CBIR) system plays a vital role to retrieve the relevant images as per the user perception from the huge database is a challenging task. Images are represented is to employ a combination of low-level features as per their visual content to form a feature vector. To reduce the search time of a large database while retrieving images, a novel image retrieval technique based on feature dimensionality reduction is being proposed with the exploit of metaheuristic optimization techniques based on Genetic Algorithm (GA), Extended Binary Cuckoo Search (EBCS) and Whale Optimization Algorithm (WOA). Each image in the database is indexed using a feature vector comprising of fuzzified based color histogram descriptor for color and Median binary pattern were derived in the color space from HSI for texture feature variants respectively. Finally, results are being compared in terms of Precision, Recall, F-measure, Accuracy, and error rate with benchmark classification algorithms (Linear discriminant analysis, CatBoost, Extra Trees, Random Forest, Naive Bayes, light gradient boosting, Extreme gradient boosting, k-NN, and Ridge) to validate the efficiency of the proposed approach. Finally, a ranking of the techniques using TOPSIS has been considered choosing the best feature selection technique based on different model parameters.