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similarity-measure
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Sparse-induced similarity measure: Mono-modal image registration via sparse-induced similarity measure
, Article IET Image Processing ; Volume 8, Issue 12 , 1 December , 2014 , Pages 728-741 ; ISSN: 17519659 ; Fatemizadeh, E ; Sharif University of Technology
Abstract
Similarity measure is an important key in image registration. Most traditional intensity-based similarity measures (e.g. sum-of-squared-differences, correlation coefficient, mutual information and correlation ratio) assume a stationary image and pixel-by-pixel independence. These similarity measures ignore the correlation among pixel intensities; hence, a perfect image registration cannot be achieved especially in the presence of spatially varying intensity distortions and outlier objects that appear in one image but not in the other. It is supposed here that non-stationary intensity distortion (such as bias field) has a sparse representation in the transformation domain. Based on this...
A new similarity measure for intensity-based image registration
, Article Proceedings of the 4th International Conference on Computer and Knowledge Engineering, ICCKE 2014 ; 18 December , 2014 , Pages 227-232 ; ISBN: 9781479954865 ; Aghajani, K ; Manzuri Shalmani, M. T ; Sharif University of Technology
Abstract
Defining a suitable similarity measure is a crucial step in (medical) image registration tasks. A common problem with frequently used intensity-based image registration algorithms is that they assume intensities of different pixels are independent of each other that could lead to low registration performance especially in the presence of spatially-varying intensity distortions, because they ignore the complex interactions between the pixel intensities. Motivated by this problem, in this paper we present a novel similarity measure which takes into account nonstationarity of the pixels intensity and complex spatially varying intensity distortions in mono-modal settings. Experimental results on...
From local similarity to global coding: An application to image classification
, Article Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Portland, OR ; 2013 , Pages 2794-2801 ; 10636919 (ISSN) ; Rabiee, H. R ; Farajtabar, M ; Ghazvininejad, M ; Sharif University of Technology
2013
Abstract
Bag of words models for feature extraction have demonstrated top-notch performance in image classification. These representations are usually accompanied by a coding method. Recently, methods that code a descriptor giving regard to its nearby bases have proved efficacious. These methods take into account the nonlinear structure of descriptors, since local similarities are a good approximation of global similarities. However, they confine their usage of the global similarities to nearby bases. In this paper, we propose a coding scheme that brings into focus the manifold structure of descriptors, and devise a method to compute the global similarities of descriptors to the bases. Given a local...
Mono-modal image registration via correntropy measure
, Article Iranian Conference on Machine Vision and Image Processing, MVIP ; Sept , 2013 , Pages 223-226 ; 21666776 (ISSN) ; 9781467361842 (ISBN) ; Fatemizadeh, E ; Sharif University of Technology
IEEE Computer Society
2013
Abstract
The registration of images is a fundamental task in numerous applications in medical image processing. Similarity measure is an important key in intensity based image registration. Here, we propose correntropy measure as similarity measure in mono modal setting. Correntropy is a important measure between two random variables based on information theoretic learning and kernel methods. This measure is useful in non-Gaussian signal processing. In this paper, this measure is used in image registration. Here, we analytically illustrate that this measure is robust in presence of spiky noise (impulsive noise). The experimental results show that the proposed similarity has better performance than...
Nonrigid registration of breast MR images using residual complexity similarity measure
, Article Iranian Conference on Machine Vision and Image Processing, MVIP, Zanjan ; Sept , 2013 , Pages 241-244 ; 21666776 (ISSN); 9781467361842 (ISBN) ; Ghaffari, A ; Fatemizadeh, E ; Sharif University of Technology
IEEE Computer Society
2013
Abstract
Elimination of motion artifact in breast MR images is a significant issue in pre-processing step before utilizing images for diagnostic applications. Breast MR Images are affected by slow varying intensity distortions as a result of contrast agent enhancement. Thus a nonrigid registration algorithm considering this effect is needed. Traditional similarity measures such as sum of squared differences and cross correlation, ignore the mentioned distortion. Therefore, efficient registration is not obtained. Residual complexity is a similarity measure that considers spatially varying intensity distortions by maximizing sparseness of the residual image. In this research, the results obtained by...
PEN: Parallel English-Persian news corpus
, Article Proceedings of the 2011 International Conference on Artificial Intelligence, ICAI 2011, 18 2011 through 21 July 2011 ; Volume 2 , July , 2011 , Pages 523-528 ; 9781601321855 (ISBN) ; ICAI 2011
2011
Abstract
Parallel corpora are the necessary resources in many multilingual natural language processing applications, including machine translation and cross-lingual information retrieval. Manual preparation of a large scale parallel corpus is a very time consuming and costly procedure. In this paper, the work towards building a sentence-level aligned English-Persian corpus in a semi-automated manner is presented. The design of the corpus, collection, and alignment process of the sentences is described. Two statistical similarity measures were used to find the similarities of sentence pairs. To verify the alignment process automatically, Google Translator was used. The corpus is based on news...
RASIM: A novel rotation and scale invariant matching of local image interest points
, Article IEEE Transactions on Image Processing ; Volume 20, Issue 12 , 2011 , Pages 3580-3591 ; 10577149 (ISSN) ; Rabiee, H. R ; Sharif University of Technology
Abstract
This paper presents a novel algorithm for matching image interest points. Potential interest points are identified by searching for local peaks in Difference-of-Gaussian (DoG) images. We refine and assign rotation, scale and location for each keypoint by using the SIFT algorithm. Pseudo log-polar sampling grid is then applied to properly scaled image patches around each keypoint, and a weighted adaptive lifting scheme transform is designed for each ring of the log-polar grid. The designed adaptive transform for a ring in the reference keypoint and the general non-adaptive transform are applied to the corresponding ring in a test keypoint. Similarity measure is calculated by comparing the...
Applying sequence alignment in tracking evolving clusters of web-sessions data: An artificial immune network approach
, Article Proceedings - 3rd International Conference on Computational Intelligence, Communication Systems and Networks, CICSyN 2011, 26 July 2011 through 28 July 2011, Bali ; 2011 , Pages 42-47 ; 9780769544823 (ISBN) ; Azmi, R ; Sharif University of Technology
2011
Abstract
Artificial Immune System (AIS) models have outstanding properties, such as learning, adaptivity and robustness, which make them suitable for learning in dynamic and noisy environments such as the web. In this study, we tend to apply AIS for tracking evolving patterns of web usage data. The definition of the similarity of web sessions has an important impact on the quality of discovered patterns. Many prevalent web usage mining approaches ignore the sequential nature of web navigations for defining similarity between sessions. We propose the use of a new web sessions' similarity measure for investigating the usage data from web access log files. In this similarity measure, in addition to the...
A webpage similarity measure for web sessions clustering using sequence alignment
, Article 2011 International Symposium on Artificial Intelligence and Signal Processing, AISP 2011, 15 June 2011 through 16 June 2011 ; June , 2011 , Pages 20-24 ; 9781424498345 (ISBN) ; Azmi, R ; Sharif University of Technology
2011
Abstract
Web sessions clustering is a process of web usage mining task that aims to group web sessions with similar trends and usage patterns into clusters. This process is crucial for effective website management, web personalization and developing web recommender systems. Accurate clustering of web sessions is highly dependent to the similarity measure defined to compare web sessions. In this paper, we propose a similarity measure for comparing web sessions. The sequential order of web navigations in sessions is considered using sequence alignment method. Furthermore, we propose to consider the usage similarity of two web sessions based on the time a user spends on a webpage, and also the frequency...
Image Registration by Mapping Defined-Functions Around Regions of Interest
, M.Sc. Thesis Sharif University of Technology ; Manzuri, Mohammad Taghi (Supervisor)
Abstract
Image registration is the process of matching two or more images captured at different times, angles or even sensors. Image registration is finding a transformation between two images. Image registration has many applications in different fields of Image Processing and machine vision. In the field of remote sensing, image registration can be used for environmental monitoring, generating graphical maps and gathering data for geogeraphical information systems, also in the field of medical image processing it can be used to detect tumor growth. Generally whenever there is need to extract information from many images; image registration is considered as an important pre-processing step....
MR Image Registration under Variant Illumination
, M.Sc. Thesis Sharif University of Technology ; Manzouri, Mohammad Taghi (Supervisor)
Abstract
Image registration is defined as matching two or more than two images which are taken from a same scene in different times, from various views and using different sensors. By matching, we mean finding the transformation function between the images. Image registration
has many application in medical domains such as analyzing changes in body limbs, supervision of the tumors growth, and robotic surgery. The challenges of image registration are unavailability of the transformation function, existence of the outliers in the images, changes in the images intensity, changes in the image geometry, noise, etc. Image registration consists of four steps: (i) feature extraction, (ii) choosing a...
has many application in medical domains such as analyzing changes in body limbs, supervision of the tumors growth, and robotic surgery. The challenges of image registration are unavailability of the transformation function, existence of the outliers in the images, changes in the images intensity, changes in the image geometry, noise, etc. Image registration consists of four steps: (i) feature extraction, (ii) choosing a...
Medical images stabilization using sparse-induced similarity measure
, Article 2015 2nd International Conference on Pattern Recognition and Image Analysis, IPRIA 2015, 11 March 2015 through 12 March 2015 ; March , 2015 ; 9781479984459 (ISBN) ; Arabshahi, S ; Ghafari, A ; Fatemizadeh, E ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2015
Abstract
Medical image stabilization has been widely used for clinical imaging modalities. Registration is a conspicuous stage for stabilizing dynamic medical images. Some of regular methods are sensitive to bias field distortion. Sparse-induced similarity measure (SISM) is a robust registering method against spatially-varying intensity distortions which is not evitable on clinical imaging instruments. This paper presents a method for registering medical images to average of captured images using SISM method to avoid spatially-varying intensity distortions like Bias field. Proposed method is compared with SSD and MI similarity measure based registrations. Results show enhancement in stabilizing...
Overlapped ontology partitioning based on semantic similarity measures
, Article 2010 5th International Symposium on Telecommunications, IST 2010, 4 December 2010 through 6 December 2010 ; 2010 , Pages 1013-1018 ; 9781424481835 (ISBN) ; Rezaeian Delui, A ; Naghibzadeh, M ; Sharif University of Technology
Abstract
Today, public awareness about the benefits of using ontologies in information processing and the semantic web has increased. Since ontologies are useful in various applications, many large ontologies have been developed so far. But various areas like publication, maintenance, validation, processing, and security policies need further research. One way to better tackle these areas is to partition large ontologies into sub partitions. In this paper, we present a new method to partition large ontologies. For the proposed method, three steps are required: (1) transforming an ontology to a weighted graph, (2) partitioning the graph with an algorithm which recognizes the most important concepts,...
Multiple attribute group decision making method using a new similarity measure in interval type-2 fuzzy sets: A case study
, Article International Journal of Mathematics in Operational Research ; Volume 9, Issue 2 , 2016 , Pages 139-166 ; 17575850 (ISSN) ; Mahdavi, I ; Mahdavi Amiri, N ; Sharif University of Technology
Inderscience Enterprises Ltd
2016
Abstract
Similarity measures are very important concepts in fuzzy sets theory. There are several definitions for similarity measures in both type-1 and interval type-2 fuzzy sets (IT2-FSs). Here, the problem of finding similarity between two IT2-FSs is considered. A new similarity measure is proposed and its performance is compared with existing approaches in literature. Furthermore, an application of this measure in multiple criteria group decision making problem is given. Based on the proposed similarity measure, the satisfaction degree for different alternatives are established which then are used to rank alternatives in multiple criteria group decision making. Finally, a case study concerning the...
Recovery of missing samples using sparse approximation via a convex similarity measure
, Article 2017 12th International Conference on Sampling Theory and Applications, SampTA 2017, 3 July 2017 through 7 July 2017 ; 2017 , Pages 543-547 ; 9781538615652 (ISBN) ; Zayyani, H ; Marvasti, F ; Anbarjafari, G ; Kivinukk, A ; Tamberg, G ; Sharif University of Technology
Abstract
In this paper, we study the missing sample recovery problem using methods based on sparse approximation. In this regard, we investigate the algorithms used for solving the inverse problem associated with the restoration of missed samples of image signal. This problem is also known as inpainting in the context of image processing and for this purpose, we suggest an iterative sparse recovery algorithm based on constrained l1-norm minimization with a new fidelity metric. The proposed metric called Convex SIMilarity (CSIM) index, is a simplified version of the Structural SIMilarity (SSIM) index, which is convex and error-sensitive. The optimization problem incorporating this criterion, is then...
Multi-modal deep distance metric learning
, Article Intelligent Data Analysis ; Volume 21, Issue 6 , 2017 , Pages 1351-1369 ; 1088467X (ISSN) ; Imani, E ; Soleymani Baghshah, M ; Sharif University of Technology
IOS Press
2017
Abstract
In many real-world applications, data contain heterogeneous input modalities (e.g., web pages include images, text, etc.). Moreover, data such as images are usually described using different views (i.e. different sets of features). Learning a distance metric or similarity measure that originates from all input modalities or views is essential for many tasks such as content-based retrieval ones. In these cases, similar and dissimilar pairs of data can be used to find a better representation of data in which similarity and dissimilarity constraints are better satisfied. In this paper, we incorporate supervision in the form of pairwise similarity and/or dissimilarity constraints into...
A novel granular approach for detecting dynamic online communities in social network
, Article Soft Computing ; 2018 ; 14327643 (ISSN) ; Zakerolhosseini, A ; Bagheri Shouraki, S ; Homayounvala, E ; Sharif University of Technology
Springer Verlag
2018
Abstract
The great surge in the research of community discovery in complex network is going on due to its challenging aspects. Dynamicity and overlapping nature are among the common characteristics of these networks which are the main focus of this paper. In this research, we attempt to approximate the granular human-inspired viewpoints of the networks. This is especially helpful when making decisions with partial knowledge. In line with the principle of granular computing, in which precision is avoided, we define the micro- and macrogranules in two levels of nodes and communities, respectively. The proposed algorithm takes microgranules as input and outputs meaningful communities in rough...
A new similarity index for nonlinear signal analysis based on local extrema patterns
, Article Physics Letters, Section A: General, Atomic and Solid State Physics ; Volume 382, Issue 5 , February , 2018 , Pages 288-299 ; 03759601 (ISSN) ; Motie Nasrabadi, A ; Shamsollahi, M. B ; Sharif University of Technology
Elsevier B.V
2018
Abstract
Common similarity measures of time domain signals such as cross-correlation and Symbolic Aggregate approximation (SAX) are not appropriate for nonlinear signal analysis. This is because of the high sensitivity of nonlinear systems to initial points. Therefore, a similarity measure for nonlinear signal analysis must be invariant to initial points and quantify the similarity by considering the main dynamics of signals. The statistical behavior of local extrema (SBLE) method was previously proposed to address this problem. The SBLE similarity index uses quantized amplitudes of local extrema to quantify the dynamical similarity of signals by considering patterns of sequential local extrema. By...
Image registration based on low rank matrix: rank-regularized SSD
, Article IEEE Transactions on Medical Imaging ; January , 2018 , Pages 138-150 ; 02780062 (ISSN) ; Fatemizadeh, E ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2018
Abstract
Similarity measure is a main core of image registration algorithms. Spatially varying intensity distortion is an important challenge, which affects the performance of similarity measures. Correlation among the pixels is the main characteristic of this distortion. Similarity measures such as sum-of-squared-differences (SSD) and mutual information ignore this correlation; hence, perfect registration cannot be achieved in the presence of this distortion. In this paper, we model this correlation with the aid of the low rank matrix theory. Based on this model, we compensate this distortion analytically and introduce rank-regularized SSD (RRSSD). This new similarity measure is a modified SSD based...
Web User Profile Clustering Using Artificial Immune System
, M.Sc. Thesis Sharif University of Technology ; Azmi, Reza (Supervisor) ; Khansari, Mohammad (Supervisor)
Abstract
Nowadays, a need for intelligent system that can assist web users in acquiring their desired information has been highlighted. In particular a mechanism for web user profiling which involves knowing the users and providing them with their preferred information is recommended to any website. To address this issue, Web Usage Mining (WUM) techniques have attracted many attentions. WUM is a kind of web mining process that tries to extract interesting usage patterns from the data that are obtained from the interaction of users with the web. The most important drawback of conventional, data mining based, WUM system is their inability to continuously learn the evolving patterns of web usage. On the...