Loading...
Search for:
image-retrieval
0.008 seconds
Total 62 records
Large scale image search
, M.Sc. Thesis Sharif University of Technology ; Moghaddasi, Reza (Supervisor)
Abstract
Searching for images of the same object or scene in a large number of images is a major problem in computer vision. It has many applications specially in the search engines.For the goal of efficient image search, we need descriptors that are not only discriminative, but also short and need small amount of memory.In this thesis we analyze the image search methods in two categories: The first methods are based on converting the existed descriptors such as gist into a compact binary code. The second methods are based on building short descriptors, especially by some modifications in the framework of bag of features descriptor.Finally we will introduce a novel descriptor "bag of codes" which...
A Self-Tag Rectifier Model for Automatic Image Annotation
,
Ph.D. Dissertation
Sharif University of Technology
;
Jamzad, Mansour
(Supervisor)
;
Beigy, Hamid
(Co-Supervisor)
Abstract
Automatic image annotation is an image retrieval mechanism to extract relative semantic tags from visual contents. The number of digital images uploaded in the virtual world is rapidly growing every day. Most of those images are not assigned with proper tags or labels. Although automatic image annotation methods are developed to assign proper tags to images, most of these methods assign some irrelevant tags and also sometimes a few relevant tags are missing. So far, the improvements of accuracy in newly developed automatic image annotation methods have been about one or two percent in F1-score compared to the previous methods. To reach much better performance, we analyzed most of the...
Content-Based Image Retrieval Using Relevance Feedback and Semi-Supervised Learning
, M.Sc. Thesis Sharif University of Technology ; Manzuri, Mohammad Taghi (Supervisor)
Abstract
Content-Based Image Retrieval has been an active research area in recent years, due to the vast amount of digital media available via the Internet. In this work we formulate contentbased image retrieval as machine learning problem using the relevance feedback technique and propose a learning algorithm adapted to specific properties of image retrieval. After studying specific properties of image retrieval as a machine learning problem, we propose a Bayesian framework for image retrieval based on one-class learning and test it on different image datasets. The proposed method is a kernel based approach and can also utilize domain knowledge in the form of prior knowledge in constructing a model...
Content Based Image Retrieval Using Segmentation Similarity Measure
,
M.Sc. Thesis
Sharif University of Technology
;
Jamzad, Mansour
(Supervisor)
Abstract
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...
Content-based Image Retrieval of Clothing Items with Neural Networks
, M.Sc. Thesis Sharif University of Technology ; Kasaei, Shohreh (Supervisor)
Abstract
One of the trends and important topics in computer vision is content based image retrieval. in this subject, we ask image as query from system, then system will search in pre-processed dataset and finds nearest images to the query and return them as result. in this thesis, our goal is to solve this problem in better way for fashion dataset. current solutions will generate bad results in case of rotation in input query or dataset. last recent years, transformers are generated really good results in NLP, then the ViT reproduced same idea in computer vision and gained comparable results due to CNNs. so, we are going to use vision transformers to solve content-based image retrieval problem with...
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
;
Jamzad, Mansoor
(Supervisor)
;
Manzuri Shalmani, Mohammad Taghi
(Co-Advisor)
Abstract
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...
Reducing Semantic Gap in Content-Based Image Retrieval Systems Using Graph Cuts and Fuzzy Relevance Feedback
,
M.Sc. Thesis
Sharif University of Technology
;
Tabandeh, Mahmoud
(Supervisor)
Abstract
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/...
Content Based Mammogram Image Retrieval Based on the Multiclass Visual Problem
, M.Sc. Thesis Sharif University of Technology ; Fatemizadeh, Emad (Supervisor)
Abstract
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...
Use of Fuzzy Type 2 in Image/Video Retrieval
, M.Sc. Thesis Sharif University of Technology ; Ghanbari, Mohammad (Supervisor)
Abstract
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...
Automatic Image Annotation by Multi-view Non-negative Matrix Factorization
, Ph.D. Dissertation Sharif University of Technology ; Jamzad, Mansour (Supervisor)
Abstract
Nowadays the number of digital images has largely increased because of progress in internet technology. Management of this volume of data needs an efficient system for browsing, categorizing, and searching the images. The goal of this research is to design a system for automatic annotation of unobserved images for better search in image data bases. Automatic image annotation is a multi-label classification problem with many labels which suggests some words for describing the content of an image. Designing AIA systems faces chanllenges like semantic gap between low level image features and high level human expressions (tags), incompelete tags and imbalance images per tags in the datasets....
Image Recovery from Random and Block Losses
, M.Sc. Thesis Sharif University of Technology ; Marvasti, Farrokh (Supervisor)
Abstract
Digital images degrade during transmittion via noisy channels. The goal of this thesis is to propose new methods for image recovery from random and block losses In the first part of the thesis, various techniques for image recovery from random losses will be reviewd and then a method will be proposed based on the correlation among image pixels in the spatial domain. The method is fast, efficient and robust against Gaussian noise. Also a technique will be developed for quality estimation in the recipient. The second part of the thesis devotes image recovery from block losses. After a brief survey for image inpainting techniques we intoduce the concept of image reconstruction using the...
A Study on Image Retrieval Methods
, M.Sc. Thesis Sharif University of Technology ; Razvan, Mohammad-Reza (Supervisor) ; Kamali-Tabrizi, Mostafa (Co-Supervisor)
Abstract
Image retrieval refers to the task of finding images related to a query image within an image set. Due to ever-increasing volumes of data, it has become increasingly necessary to find suitable and efficient methods for searching in massive databases. In this thesis, modern image retrieval techniques developed within the last 15 years have been studied, with an aim to satisfy three primary constraints of efficiency, accuracy, and low memory usage. Our focus has been on content-based retrieval; meaning that instead of using text and other information, we directly utilize image features for analysis and processing. To achieve this, we studied two established techniques, the bag-of-words model,...
Design a Content-Based Color Image Retrieval Using Attention Driven Saliency Map
, M.Sc. Thesis Sharif University of Technology ; Fatemizadeh, Emadeddin (Supervisor)
Abstract
Content Based Image Retrieval (CBIR) is in fact an image search engine which Operates on image Context . in this thesis (project) the aim was to use the Visual attention of humans in detecting the objects in image. in this ability first a salient image of the most important things in the image would be created And after an initial separation , for the final recognition the other features (details) in the image will be used It’s a while that the use of Visual attention models and saliency maps in designing the interfaces between humans and machines has been considered widely. This fact in the design of CBIR systems has not a good background (satisfying history). In this thesis I have...
Achieving higher perceptual quality and robustness in watermarking using Julian set patterns
, Article IEE Proceedings: Information Security ; Volume 153, Issue 4 , 2006 , Pages 167-172 ; 17470722 (ISSN) ; Jamzad, M ; Sharif University of Technology
2006
Abstract
Some of the most important classes of watermark detection methods in image watermarking are correlation-based algorithms. In these methods usually a pseudorandom noise pattern is embedded in the host image. The receiver can regenerate this pattern by having a key that is the seed of a random number generator. After that if the correlation between this pattern and the image that is assumed to have the watermark is higher than a predefined threshold, it means that the watermark exists and vice versa. Here, we show the advantage of using the Julian set patterns as a watermark, instead of the commonly used pseudorandom noise pattern. Julian set patterns can be regenerated in receiver with few...
Unsupervised estimation of conceptual classes for semantic image annotation
, Article 2011 19th Iranian Conference on Electrical Engineering, ICEE 2011, 17 May 2011 through 19 May 2011 ; May , 2011 ; 9789644634284 (ISBN) ; Esmaili, H ; Shirazi, A. A. B ; Sharif University of Technology
2011
Abstract
A probabilistic formulation for semantic image annotation and retrieval is proposed. Annotation and retrieval are posed as classification problems where each class is defined as the group of database images labeled with a common semantic label. It is shown that, by establishing this one-to-one correspondence between semantic labels and semantic classes, a minimum probability of error annotation and retrieval are feasible with algorithms that are 1) conceptually simple and 2) computationally efficient. In this article, a content-based image retrieval and annotation architecture is proposed. Its attitude is decreasing the semantic gap by partitioning the image to its semantic regions and using...
A framework for content-based human brain magnetic resonance images retrieval using saliency map
, Article Biomedical Engineering - Applications, Basis and Communications ; Volume 25, Issue 4 , 2013 ; 10162372 (ISSN) ; Fatemizadeh, E ; Badie, K ; Sharif University of Technology
2013
Abstract
Content-based image retrieval (CBIR) makes use of low-level image features, such as color, texture and shape, to index images with minimal human interaction. Considering the gap between low-level image features and the high-level semantic concepts in the CBIR, we proposed an image retrieval system for brain magnetic resonance images based on saliency map. The saliency map of an image contains important image regions which are visually more conspicuous by virtue of their contrast with respect to surrounding regions. First, the proposed approach exploits the ant colony optimization (ACO) technique to measure the image's saliency through ants' movements on the image. The textural features are...
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) ; Fatemizadeh, E ; Badie, K ; Sharif University of Technology
2013
Abstract
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...
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) ; Fatemizadeh, E ; Badie, K ; Sharif University of Technology
2013
Abstract
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) ; Fatemizadeh, E ; Badie, K ; Sharif University of Technology
2012
Abstract
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...
Hierarchical concept score post-processing and concept-wise normalization in CNN based video event recognition
, Article IEEE Transactions on Multimedia ; Volume: 21 , Issue: 1 , Jan , 2019 , 157 - 172 ; 15209210 (ISSN) ; Ghaemmaghami, S ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2018
Abstract
This paper is focused on video event recognition based on frame level CNN descriptors. Using transfer learning, the image trained descriptors are applied to the video domain to make event recognition feasible in scenarios with limited computational resources. After fine-tuning of the existing Convolutional Neural Network (CNN) concept score extractors, pre-trained on ImageNet, the output descriptors of the different fully connected layers are employed as frame descriptors. The resulting descriptors are hierarchically post-processed and combined with novel and efficient pooling and normalization methods. As major contributions of this work to the video event recognition, we present a...