• Asia pacific journal of information systems
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• v.33 no.3
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• pp.768-791
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• 2023

Effective brand management using images has been a challenge for the brand managers. The brand identity-brand image alignment on the social media is an important yet mostly-overlooked phenomenon. We proposed a scalable Google Cloud Vision-based approach for measuring the alignment between brand identity and brand image, and understanding the brand positions. We analyzed 3247 images of 13 leading Indian brands on Instagram. Images containing wordy announcements by the firms are in stark contrast with the relatively more emotive images by the users. It leads to a noticeable disconnect between the brand identity and brand image. Also, the private sector brands do not always outperform the public sector brands in branding efforts. By offering practical guidance on how to measure and reduce the misalignment, this study paved a feasible path towards better visual branding on Instagram.

• Journal of the Korean Society for Library and Information Science
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• v.47 no.4
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• pp.275-294
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• 2013

This study utilizes structural equation modeling process to identify contributing factors that have been extracted through the first study of exploring library service and image building related factors. This study, specifically, tests the relationships between endogenous and exogenous variables that are assumed to have inherent relationships when building public library service image. As denoted from the first study, this one uses three dimensions and nine conceptual level constructs and 20 different measurement items for further test. The results of this study is that particular items to measure the image for the users would not have been fitted to the other set of samples. Also, there are differences between the two employee groups in the recognitions of images.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.295-299
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• 2002

This paper presents an image contrast enhancement technique for improving low contrast images. We applied fuzzy logic to develop an image contrast enhancement technique in the viewpoint of considering that the low pictorial information of a low contrast image is due to the vaguness or fuzziness of the multivalued levels of brightness rather than randomness. The fuzzy image contrast enhancement technique consists of three main stages, namely, image fuzzification, modification of membership values, and image defuzzification. In the stage of image fuzzification, we need to select a crossover point. To select the crossover point automatically the K-means algorithm is used. The problem of crossover point selection can be considered as the two-category, object and background, classification problem. The proposed method is applied to an experimental image with 256 gray levels and the result of the proposed method is compared with that of the histogram equalization technique. We used the index of fuzziness as a measure of image quality. The result shows that the proposed method is better than the histogram equalization technique.

• Journal of Broadcast Engineering
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• v.26 no.7
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• pp.838-843
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• 2021

This paper presents an algorithm for effective retrieval of score information to an input score image. The originality of the proposed algorithm is that it is designed to be robust to recognition errors by an OMR (Optical Music Recognition), while existing methods such as pitch histogram requires error induced OMR result be corrected before retrieval process. This approach helps people to retrieve score without training on music score for error correction. OMR takes a score image as input, recognizes musical symbols, and produces structural symbolic notation of the score as output, for example, in MusicXML format. Among the musical symbols on a score, it is observed that filled noteheads are rarely detected with errors with its simple black filled round shape for OMR processing. Barlines that separate measures also strong to OMR errors with its long uniform length vertical line characteristic. The proposed algorithm consists of a descriptor for a score and a similarity measure between a query score and a reference score. The descriptor is based on note-count, the number of filled noteheads in a measure. Each part of a score is represented by a sequence of note-count numbers. The descriptor is an n-gram sequence of the note-count sequence. Simulation results show that the proposed algorithm works successfully to a certain degree in score image-based retrieval for an erroneous OMR output.

• Journal of Korea Multimedia Society
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• v.17 no.7
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• pp.849-857
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• 2014

In this paper, we propose a (2,2)-reversible secret image sharing scheme using histogram shifting and difference expansion. Two techniques are widely used in information hiding. Advantages of them are the low distortion between cover and stego images, and high embedding capacity. In secret image sharing procedure, unlike Shamir's secret sharing, a histogram generate that the difference value between the original image and copy image is computed by difference expansion. And then, the secret image is embedded into original and copy images by using histogram shifting. Lastly, two generated shadow images are distributed to each participant by the dealer. In the experimental results, we measure a capacity of a secret image and a distortion ratio between original image and shadow image. The results show that the embedding capacity and image distortion ratio of the proposed scheme are superior to the previous schemes.

• International Journal of Computer Science & Network Security
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• v.21 no.12
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• pp.65-74
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• 2021

Digital image processing and retrieving have increasingly become very popular on the Internet and getting more attention from various multimedia fields. That results in additional privacy requirements placed on efficient image matching techniques in various applications. Hence, several searching methods have been developed when confidential images are used in image matching between pairs of security agencies, most of these search methods either limited by its cost or precision. This study proposes a secure and efficient method that preserves image privacy and confidentially between two communicating parties. To retrieve an image, feature vector is extracted from the given query image, and then the similarities with the stored database images features vector are calculated to retrieve the matched images based on an indexing scheme and matching strategy. We used a secure content-based image retrieval features detector algorithm called Speeded-Up Robust Features (SURF) algorithm over public cloud to extract the features and the Honey Encryption algorithm. The purpose of using the encrypted images database is to provide an accurate searching through encrypted documents without needing decryption. Progress in this area helps protect the privacy of sensitive data stored on the cloud. The experimental results (conducted on a well-known image-set) show that the performance of the proposed methodology achieved a noticeable enhancement level in terms of precision, recall, F-Measure, and execution time.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.3C
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• pp.112-118
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• 2005

In this paper, we propose a new nonlinear matching measure for automatic analysis of the on-off type DNA microarray images in which the hybridized spots are detected by the template matching method. The proposed measure is obtained by binary-thresholding over the whole template region and taking the number of white pixels inside the spotted area. This measure is compared with the normalized covariance in terms of the classification ability of the successfulness of the locating markers. The proposed measure is evaluated for the scanned images of HPV DNA microarrays where the marker locating is a critical issue because of the small number of spots. The targeting spots of HPV DNA chips are designed for genotyping 22 types of the human papilloma virus(HPV). The proposed measure is proven to give more discriminative response reducing the miss cases of the successful marker locating.

• Journal of KIISE:Databases
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• v.36 no.5
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• pp.366-373
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• 2009

In this paper, we propose a similarity search algorithm for image databases. One of the central problems regarding content-based image retrieval (CBIR) is the semantic gap between the low-level features computed automatically from images and the human interpretation of image content. Many search algorithms used in CBIR have used the Minkowski metric (or $L_p$-norm) to measure similarity between image pairs. However those functions cannot adequately capture the aspects of the characteristics of the human visual system as well as the nonlinear relationships in contextual information. Our new search algorithm tackles this problem by employing new similarity measures and ranking strategies that reflect the nonlinearity of human perception and contextual information. Our search algorithm yields superior experimental results on a real handwritten digit image database and demonstrates its effectiveness.

• Journal of Information Processing Systems
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• v.15 no.4
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• pp.737-747
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• 2019

Shape description is an important and fundamental issue in content-based image retrieval (CBIR), and a number of shape description methods have been reported in the literature. For shape description, both global information and local contour variations play important roles. In this paper a new included-angular ternary pattern (IATP) based shape descriptor is proposed for shape image retrieval. For each point on the shape contour, IATP is derived from its neighbor points, and IATP has good properties for shape description. IATP is intrinsically invariant to rotation, translation and scaling. To enhance the description capability, multiscale IATP histogram is presented to describe both local and global information of shape. Then multiscale IATP histogram is combined with included-angular histogram for efficient shape retrieval. In the matching stage, cosine distance is used to measure shape features' similarity. Image retrieval experiments are conducted on the standard MPEG-7 shape database and Swedish leaf database. And the shape image retrieval performance of the proposed method is compared with other shape descriptors using the standard evaluation method. The experimental results of shape retrieval indicate that the proposed method reaches higher precision at the same recall value compared with other description method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.279-282
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• 2001

This paper presents an image contrast enhancement technique for improving the low contrast images using the improved IAFC(Integrated Adaptive Fuzzy Clustering) Model. The low pictorial information of a low contrast image is due to the vagueness or fuzziness of the multivalued levels of brightness rather than randomness. Fuzzy image processing has three main stages, namely, image fuzzification, modification of membership values, and image defuzzification. Using a new model of automatic crossover point selection, optimal crossover point is selected automatically. The problem of crossover point selection can be considered as the two-category classification problem. The improved MEC can classify the image into two classes with unsupervised teaming rule. The proposed method is applied to some experimental images with 256 gray levels and the results are compared with those of the histogram equalization technique. We utilized the index of fuzziness as a measure of image quality. The results show that the proposed method is better than the histogram equalization technique.