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Efficient Image Retrieval by Fuzzy Rules from Boosting and Metaheuristic

.R., Arabas, J.: Differential Evolution: A survey of theoretical analyses. Swarm and Evolutionary Computation (2018).. DOI: https://doi.org/10.1016/j.swevo.2018.06.010 [47] Pass, G., Zabih, R.: Histogram refinement for content-based image retrieval. In: Applications of Computer Vision, 1996. WACV ’96., Proceedings 3rd IEEE Workshop on, pp. 96–102 (1996). DOI 10.1109/ACV.1996.572008 [48] Philbin, J., Chum, O., Isard, M., Sivic, J., Zisserman, A.: Object retrieval with large vocabularies and fast spatial matching. In: Computer Vision and Pattern Recognition, 2007

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A simple multi-feature based stereoscopic medical image retrieval system

Project: Querying Images by Content, Using Color, Texture, and Shape. SPIE Conf on Storage and Retrieval for Image and Video Databases. 1993;1908.173-187. [4] Welter P, Riesmeier J, Fischer B, et al. Bridging the integration gap between imaging and information systems: a uniform data concept for content-based image retrieval in computer aided diagnosis. J American Med Informatics Association. 2011;18(4):506-510. [5] Lehmann TM, Wein B, Dahmen J, et al. Content-based Image Retrieval in Medical Applications: A Novel Multi-step Approach. Proces of SPIE - The

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Content-based image retrieval using a signature graph and a self-organizing map

References Abdesselam, A., Wang, H.H. and Kulathuramaiyer, N. (2010). Spiral bit-string representation of color for image retrieval, International Arab Journal of Information Technology 7(3): 223-230. Acharya, T. and Ray, A.K. (2005). Image Processing: Principles and Applications, John Wiley and Sons, Hoboken, NJ. Alzu’bi, A., Amira, A. and Ramzan, N. (2015). Semantic content-based image retrieval: A comprehensive study, Journal of Visual Communication and Image Representation 32: 20-54. Bahri, A

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Deep Learning for Plant Classification and Content-Based Image Retrieval

Abstract

The main goal of the present research is to classify images of plants to species with deep learning. We used convolutional neural network architectures for feature learning and fully connected layers with logsoftmax output for classification. Pretrained models on ImageNet were used, and transfer learning was applied. In the current research image sets published in the scope of the PlantCLEF 2015 challenge were used. The proposed system surpasses the results of all top competitors of the challenge by 8% and 7% at observation and image levels, respectively. Our secondary goal was to satisfy the users’ needs in content-based image retrieval to give relevant hits during species search task. We optimized the length of the returned lists in order to maximize MAP (Mean Average Precision), which is critical to the performance of image retrieval. Thus, we achieved more than 50% improvement of MAP in the test set compared to the baseline.

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Image Retrieval Algorithm Based on Discrete Fractional Transforms

The discrete fractional transforms is a signal processing tool which suggests computational algorithms and solutions to various sophisticated applications. In this paper, a new technique to retrieve the encrypted and scrambled image based on discrete fractional transforms has been proposed. Two-dimensional image was encrypted using discrete fractional transforms with three fractional orders and two random phase masks placed in the two intermediate planes. The significant feature of discrete fractional transforms benefits from its extra degree of freedom that is provided by its fractional orders. Security strength was enhanced (1024!)4 times by scrambling the encrypted image. In decryption process, image retrieval is sensitive for both correct fractional order keys and scrambling algorithm. The proposed approach make the brute force attack infeasible. Mean square error and relative error are the recital parameters to verify validity of proposed method.

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Enhancing the Precision of Walsh Wavelet Based Approach for Color and Texture Feature Extraction in CBIR by Including a Shape Feature

References 1. Kekre, H. B., S. D. Thepade, A. Athawale, A. Shah, P. Verlekar, S. Shirke. Kekre Transform over Row Mean, Column Mean and Both Using Image Tiling for Image Retrieval. - International Journal of Computer and Electrical Engineering, Vol. 2, December 2010, No 6. 2. Kekre, H. B., T. K. Sarode, S. D. Thepade, S. Sanas. Assorted Color Spaces to Improve the Image Retrieval using VQ Codebooks Generated Using LBG and KEVR. - In: Proc. of the International Conference on Technology Systems and Management (ICTSM’2011

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Content-Based Image Retrieval for Multiple Objects Search

References 1. Abdi, H., L. J. Williams. Principal Component Analysis. – Wiley Interdisciplinary Reviews: Computational Statistics, Vol. 2 , 2010, No 4, pp. 433-459. 2. Arandjelovic, R., A. Zisserman. Three Things Everyone Should Know to Improve Object Retrieval. – In: Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012, pp. 2911-2918. 3. Banda, J. M., R. A. Angryk, P. C. Martens. Image FARMER: Introducing a Data Mining Framework for the Creation of Large-Scale Content-Based Image Retrieval Systems. – International

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A Comparative Study of SIFT and its Variants

SIFT is an image local feature description algorithm based on scale-space. Due to its strong matching ability, SIFT has many applications in different fields, such as image retrieval, image stitching, and machine vision. After SIFT was proposed, researchers have never stopped tuning it. The improved algorithms that have drawn a lot of attention are PCA-SIFT, GSIFT, CSIFT, SURF and ASIFT. In this paper, we first systematically analyze SIFT and its variants. Then, we evaluate their performance in different situations: scale change, rotation change, blur change, illumination change, and affine change. The experimental results show that each has its own advantages. SIFT and CSIFT perform the best under scale and rotation change. CSIFT improves SIFT under blur change and affine change, but not illumination change. GSIFT performs the best under blur change and illumination change. ASIFT performs the best under affine change. PCA-SIFT is always the second in different situations. SURF performs the worst in different situations, but runs the fastest.

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Crisp and Fuzzy Classifiers in the Two-Phase Gas-Liquid Flow Diagnostics

for similar image retrieval, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7267 LNAI (PART 1), pp. 509-517, 2012 [4] Z. Ji, Y. Xia, Q. Chen, Q. Sun, D. Xia, D.D. Feng, Fuzzy c-means clustering with weighted image patch for image segmentation, Applied Soft Computing Journal, Vol. 12, No. 6, pp. 1659-1667, 2012 [5] S.R. Kannan, S. Ramathilagam, P.C. Chung, Effective fuzzy c-means clustering algorithms for data clustering problems, Expert Systems with

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