Search for: content-based-retrieval
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    SM3D studio: A 3D model constructor

    , Article Iranian Conference on Machine Vision and Image Processing, MVIP ; 2013 , Pages 10-15 ; 21666776 (ISSN); 9781467361842 (ISBN) Soleimani, V ; Vincheh, F. H ; Zare, E ; Engineers (IEEE) Antennas and Propagation Society; The Institute of Electrical and Electronics ; Sharif University of Technology
    IEEE Computer Society  2013
    In this paper we describe designing and implementation of a powerful, fast and compact simple 3D modeler (SM3D). In addition to saving cost and time (due to high processing speed), 3D objects can be created with minimum system resources by using this application. Easy learning and using are other strengths of this application. Modularity using classification and applying Dynamic-Link Library files are noted aspects that are regarded in writing the source code and this causes separation of main part and user interface, so the application can be easily expanded in the future. Ability to create primary objects and also applying advanced transformations and modifiers have been considered.... 

    Adaptive batch steganography considering image embedding capacity

    , Article Optical Engineering ; Volume 48, Issue 8 , 2009 ; 00913286 (ISSN) Sajedi, H ; Jamzad, M ; Sharif University of Technology
    The problem of spreading secret data to embed into multiple cover images is called batch steganography and has been theoretically considered recently. Few works have been done in batch steganography, and in all of them, the payload is spread between cover images unwisely. We present the Adaptive batch steganography (ABS) approach and consider embedding capacity as a property of images. ABS is an approach to adaptively spread secret data among multiple cover images based on their embedding capacity. By splitting the payload based on image embedding capacity constraint, embedding can be done more secure than the state when the embedder does not know how much data can be hidden securely in an... 

    Image Classication for Content Based Image Retrieval

    , M.Sc. Thesis Sharif University of Technology Saboorian, Mohammad Mehdi (Author) ; Jamzad, Mansour (Supervisor)
    In this project we tried to to solve the problem of clustring images of a large image database. Considering that there is no prior information regarding domain of the images, we will review unsupervised clustring methods. For this, we will discuss about image description vector and similarity measures. At last, our contribution will be about finding the optimum number of clusters with the least of user invervention. Results of runnig our method on a databse with 1000 images is reported and compared to a similar method named CLUE. Our result shows considerable improvements when user feedback taken to account.

    A CBIR System for Human Brain Magnetic Resonance Image Indexing

    , M.Sc. Thesis Sharif University of Technology Rafi Nazari, Mina (Author) ; Fatemizadeh, Emadeddin (Supervisor)
    Content-based image retrieval (CBIR) is becoming an important field with the advance of multimedia and imaging technology everincreasingly. It makes use of image features, such as color, shape and texture, to index images with minimal human intervention. Among many retrieval features associated with CBIR, texture retrieval is one of the most powerful. Content-based image retrieval can also be utilized to locate medical images in large databases. In this research, we introduce a content-based approach to medical image retrieval. A case study, which describes the methodology of a CBIR system for retrieving digital human brain MRI database based on textural features retrieval, is then... 

    Reducing Semantic Gap in Content-Based Image Retrieval Systems Using Graph Cuts and Fuzzy Relevance Feedback

    , M.Sc. Thesis Sharif University of Technology Shafeian, Hessamoddin (Author) ; Tabandeh, Mahmoud (Supervisor)
    Multimedia retrieval systems are gradually playing a critical role in our everyday life to facilitate interacting with massive amount of personal or professional images, music and video archives. So far, many systems have been proposed among them relevance feedback based content based multimedia (especially image) retrievals has been proved to be more effective. However there is still a problem called semantic gap, in finding proper mapping between low-level features used by CBIR systems and user’s high-level concepts. On the other hand graph cuts have been a great powerful tool for solving many computer vision problems. They benefit from robust optimization algorithm called maximum flow/... 

    Use of Fuzzy Type 2 in Image/Video Retrieval

    , M.Sc. Thesis Sharif University of Technology Rasekh Langr, Hadi (Author) ; Ghanbari, Mohammad (Supervisor)
    In content based image retrieval, low level features are used to find similar image. To do this, many system has been proposed by other people, in which in many of them, combination of features in the same time are used as a step of retrieval to increase accuracy. Feature combination are divied in two category: vector based and weight based which in weight based approach, features can get different weight, based on their importance and role in retrieval accuracy. Each image contain different partition, which some of them like background, base on their lower discrimintivity power, have lower importance. Based on our study, some image have powerfull color features and some of them have... 

    Content Based Image Retrieval Using Segmentation Similarity Measure

    , M.Sc. Thesis Sharif University of Technology Farhadi, Marzyieh (Author) ; Jamzad, Mansour (Supervisor)
    Content Based Image Retrieval (CBIR) is a research area in computer vision. This area comprises of two main steps, low level feature extraction such as color, texture and shape extraction and also similarity measures for comparison of images. The challenge in this system is the existence semantic gap between the low level visual features and the high level image semantics. The aim of research in this field is to reduce this semantic gap. In this study the images are divided into regions using Meanshift method, for color segmentation and then moments of each region as color feature are calculated. Also for extracting texture the images are divided into regions using Jseg method, and then... 

    Designing a Video Search System Using Topic Models

    , M.Sc. Thesis Sharif University of Technology Kianpisheh, Mohammad (Author) ; Gholampoor, Iman (Supervisor) ; Sharif Khani, Mohammad (Supervisor)
    In this work we present a surveillance video retrieval system based on Topic Models. We’ve shown that employing Dynamic Programming improve the effectiveness of Topic Model based retrival. In the other hand proposed method has the accuracy near to the low-level features based methods. Lightweight database is the major advantageous of propsed method over the low-level features based methods. In our work storage space occupied by database decreases from 42 MB to only 0.4 MB for the mit dataset. Moreover lightweight database strikingly speeded up the retrieval process, for example retrieval process in proposed method is about 24 times faster than low-level features based systems. Furthermore in... 

    The effect of a two steps searching mechanism Using Feature Vectors Related to Image Class in Improving the Performance of CBIR System

    , M.Sc. Thesis Sharif University of Technology Sherafati, Shima (Author) ; Jamzad, Mansoor (Supervisor) ; Manzuri Shalmani, Mohammad Taghi (Co-Advisor)
    Nowadays, retrieval is an inseparable part of user activities and due to growing usage of Content-Based Image Retrieval (CBIR), it has become a hot and challenging research topic specially in the past decade. The most important challenge that retrieval systems (including CBIR systems) are facing is the semantic gap between abstractions in the user’s mind and what is searched. One of the ways of dealing with this challenge is getting more information from the user about what he needs and so decreasing the distance between user’s will and what he gives to search engine as the description of his need. In this research, the class of query image is supposed to be given. For using this... 

    Content Based Mammogram Image Retrieval Based on the Multiclass Visual Problem

    , M.Sc. Thesis Sharif University of Technology Siyahjani, Farzad (Author) ; Fatemizadeh, Emad (Supervisor)
    In recent years there has been a great effort to enhance the computer-aided diagnosis systems, Since expertise elicited from past resolved cases plays an important role in medical applications, and images acquired from various cases have a great contribution to diagnosis of the abnormalities, Content based medical image retrieval has become an active research area for many scientists. In this project we proposed a new framework to retrieve visually similar images from a large database, in which visual similarity is regarded as much as the semantic category relevance, we used optimized wavelet transform as the multi-resolution analysis of the images and extracted various statistical SGLDM... 

    DWM-CDD: Dynamic weighted majority concept drift detection for spam mail filtering

    , Article World Academy of Science, Engineering and Technology ; Volume 80 , 2011 , Pages 291-294 ; 2010376X (ISSN) Nosrati, L ; Pour, A. N ; Sharif University of Technology
    Although e-mail is the most efficient and popular communication method, unwanted and mass unsolicited e-mails, also called spam mail, endanger the existence of the mail system. This paper proposes a new algorithm called Dynamic Weighted Majority Concept Drift Detection (DWM-CDD) for content-based filtering. The design purposes of DWM-CDD are first to accurate the performance of the previously proposed algorithms, and second to speed up the time to construct the model. The results show that DWM-CDD can detect both sudden and gradual changes quickly and accurately. Moreover, the time needed for model construction is less than previously proposed algorithms  

    Multi-modal deep distance metric learning

    , Article Intelligent Data Analysis ; Volume 21, Issue 6 , 2017 , Pages 1351-1369 ; 1088467X (ISSN) Roostaiyan, S. M ; Imani, E ; Soleymani Baghshah, M ; Sharif University of Technology
    IOS Press  2017
    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... 

    Content based image retrieval using the knowledge of texture, color and binary tree structure

    , Article 2009 Canadian Conference on Electrical and Computer Engineering, CCECE '09, St. Johns, NL, 3 May 2009 through 6 May 2009 ; 2009 , Pages 999-1003 ; 08407789 (ISSN); 9781424435081 (ISBN) Mansoori, Z ; Jamzad, M ; Sharif University of Technology
    Content base image retrieval is an important research field with many applications. In this paper we presents a new approach for finding similar images to a given query, in a general-purpose image database using content-based image retrieval. Color and texture are used as basic features to describe images. In addition, a binary tree structure is used to describe higher level features of an image. It has been used to keep information about separate segments of the images. The performance of the proposed system has been compared with the SIMPLIcity system using COREL image database. Our experimental results showed that among 10 image categories available in COREL database, our system had a... 

    A content-based approach to medical images retrieval

    , Article International Journal of Healthcare Information Systems and Informatics ; Volume 8, Issue 2 , 2013 , Pages 15-27 ; 15553396 (ISSN) Tarjoman, M ; Fatemizadeh, E ; Badie, K ; Sharif University of Technology
    Content-based image retrieval (CBIR) makes use of image features, such as color, texture or shape, to index images with minimal human intervention. Content-based image retrieval can be used to locate medical images in large databases. In this paper, the fundamentals of the key components of content-based image retrieval systems are introduced first to give an overview of this area. Then, a case study which describes the methodology of a CBIR system for retrieving human brain magnetic resonance images, is presented. The proposed method is based on Adaptive Neuro-fuzzy Inference System (ANFIS) learning and could classify an image as normal and tumoral. This research uses the knowledge of CBIR... 

    Linear and nonlinear model of cutting forces in peripheral milling: A comparison between the accuracy of 2D and 3D models

    , Article 2009 ASME International Mechanical Engineering Congress and Exposition, IMECE2009, Lake Buena Vista, FL, 13 November 2009 through 19 November 2009 ; Volume 3 , 2010 , Pages 955-962 ; 9780791843765 (ISBN) Moradi, H ; Movahhedy, M. R ; Vossoughi, G ; Sharif University of Technology
    Peripheral milling is extensively used in manufacturing processes, especially in aerospace industry where end mills are used for milling of wing parts and engine components. Knowledge of the cutting forces is the first necessary stage in analysis of the milling process. In this paper, cutting forces are presented for both two and three dimensional models. Instead of the common linear dependency of cutting forces to the cut chip thickness, two nonlinear models are presented. In the first model, cutting forces are considered as a function of chip thickness with a complete third order polynomial. In the second one, the quadratic and constant terms of the third order polynomial are set to zero.... 

    User adaptive clustering for large image databases

    , Article Proceedings - International Conference on Pattern Recognition, 23 August 2010 through 26 August 2010, Istanbul ; 2010 , Pages 4271-4274 ; 10514651 (ISSN) ; 9780769541099 (ISBN) Saboorian, M. M ; Jamzad, M ; Rabiee, H. R ; Sharif University of Technology
    Searching large image databases is a time consuming process when done manually. Current CBIR methods mostly rely on training data in specific domains. When source and domain of images are unknown, unsupervised methods provide better solutions. In this work, we use a hierarchical clustering scheme to group images in an unknown and large image database. In addition, the user should provide the current class assignment of a small number of images as a feedback to the system. The proposed method uses this feedback to guess the number of required clusters, and optimizes the weight vector in an iterative manner. In each step, after modification of the weight vector, the images are reclustered. We... 

    An implementation of a CBIR system based on SVM learning scheme

    , Article Journal of Medical Engineering and Technology ; Volume 37, Issue 1 , 2013 , Pages 43-47 ; 03091902 (ISSN) Tarjoman, M ; Fatemizadeh, E ; Badie, K ; Sharif University of Technology
    Content-based image retrieval (CBIR) has been one of the most active areas of research. The retrieval principle of CBIR systems is based on visual features such as colour, texture and shape or the semantic meaning of the images. A CBIR system can be used to locate medical images in large databases. This paper presents a CBIR system for retrieving digital human brain magnetic resonance images (MRI) based on textural features and the support vector machine (SVM) learning method. This system can retrieve similar images from the database in two groups: normal and tumoural. This research uses the knowledge of the CBIR approach to the application of medical decision support and discrimination... 

    An interactive cbir system based on anfis learning scheme for human brain magnetic resonance images retrieval

    , Article Biomedical Engineering - Applications, Basis and Communications ; Volume 24, Issue 1 , 2012 , Pages 27-36 ; 10162372 (ISSN) Tarjoman, M ; Fatemizadeh, E ; Badie, K ; Sharif University of Technology
    Content-based image retrieval (CBIR) has turned into an important and active potential research field with the advance of multimedia and imaging technology. It makes use of image features, such as color, texture and shape, to index images with minimal human intervention. A CBIR system can be used to locate medical images in large databases. In this paper we propose a CBIR system which describes the methodology for retrieving digital human brain magnetic resonance images (MRI) based on textural features and the Adaptive neuro-fuzzy inference system (ANFIS) learning to retrieve similar images from database in two categories: normal and tumoral. A fuzzy classifier has been used, because of the...