Retrieval of Multimedia Information Using Content-Based Image Retrieval (CBIR) Techniques

Biometrics ◽  
2017 ◽  
pp. 652-689
Author(s):  
Anupam Mukherjee
Keyword(s):  
Image Retrieval ◽  
Multimedia Information ◽  
Visual Information ◽  
Video Retrieval ◽  
Automatic Generation ◽  
Content Based Image Retrieval ◽  
Manual Labor ◽  
Multidimensional Indexing ◽  
Other Information ◽  
Human Operators

This chapter will focus on the concept of Content-based image retrieval. Searching of an image or video database based on text based description is a manual labor intensive process. Descriptions of the file are usually typed manually for each image by human operators because the automatic generation of keywords for the images is difficult without incorporation of visual information and feature extraction. This method is impractical in today's multimedia information era. “Content-based” means that the search will analyze the actual contents of the image rather than the metadata such as keywords, tags, and descriptions associated with the image. The term “content” in this context might refer to colors, shapes, textures, or any other information that can be derived from the image itself. Several important sections are highlighted in this chapter, like architectures, query techniques, multidimensional indexing, video retrieval and different application sections of CBIR.

Retrieval of Multimedia Information Using Content-Based Image Retrieval (CBIR) Techniques

2017 ◽  
pp. 143-180
Author(s):  
Anupam Mukherjee
Keyword(s):  
Image Retrieval ◽  
Multimedia Information ◽  
Visual Information ◽  
Video Retrieval ◽  
Automatic Generation ◽  
Content Based Image Retrieval ◽  
Manual Labor ◽  
Multidimensional Indexing ◽  
Other Information ◽  
Human Operators

This chapter will focus on the concept of Content-based image retrieval. Searching of an image or video database based on text based description is a manual labor intensive process. Descriptions of the file are usually typed manually for each image by human operators because the automatic generation of keywords for the images is difficult without incorporation of visual information and feature extraction. This method is impractical in today's multimedia information era. “Content-based” means that the search will analyze the actual contents of the image rather than the metadata such as keywords, tags, and descriptions associated with the image. The term “content” in this context might refer to colors, shapes, textures, or any other information that can be derived from the image itself. Several important sections are highlighted in this chapter, like architectures, query techniques, multidimensional indexing, video retrieval and different application sections of CBIR.

scholarly journals FEATURE SELECTION USING ASSOCIATION RULES FOR CBIR AND COMPUTER AIDED MEDICAL DIAGNOSTIC

2015 ◽  
pp. 223-227
Author(s):  
C. BHUVANESWARI ◽  
P. ARUNA ◽  
D. LOGANATHAN
Keyword(s):  
Image Retrieval ◽  
Classification Accuracy ◽  
Visual Information ◽  
Naive Bayes ◽  
Diagnostic Techniques ◽  
Modern Medicine ◽  
Naïve Bayes ◽  
Content Based Image Retrieval ◽  
Sudden Increase ◽  
Computer Aided

Digital images are now the basis of visual information in medical applications. The advent of radiology which employs imaging for diagnosis generates great amount of images. Automatic retrieval of images based on features like color, shape and texture is termed Content Based Image Retrieval. The increasing dependence of modern medicine on diagnostic techniques such as radiology, computerized tomography has resulted in a sudden increase in the number and significance of medical images. Content Based Image Retrieval techniques are being extensively used to aid diagnosis by comparing with similar past cases and improvising Computer Aided Diagnosis. In this paper, it is proposed to extract features in the frequency domain using Walsh Hadamard transform and use FP-Growth association rule mining to extract features based on confidence. The extracted features are classified using Naïve Bayes and CART algorithms and the proposed method’s classification accuracy is evaluated. Experimental results show that classification accuracy for Naïve Bayes is 100 and 96.8 for CART on application of proposed method.

scholarly journals A REVIEW ON MULTIQUERY SYSTEM FOR CONTENT BASED IMAGE RETRIEVAL

2016 ◽  
Vol 15 (13) ◽  
pp. 7342-7346
Author(s):  
Meenu Meenu ◽  
Sonika Jindal
Keyword(s):  
Image Retrieval ◽  
Visual Information ◽  
Retrieval System ◽  
Image Similarity ◽  
Content Based Image Retrieval ◽  
Color Similarity ◽  
Image Retrieval System ◽  
Speed Up ◽  
Similarity Computation ◽  
Main Components

In recent years, very large collections of images and videos have grown rapidly. In parallel with this growth, content-based retrieval and querying the indexed collections are required to access visual information. Two of the main components of the visual information are texture and color. In this thesis, a content-based image retrieval system is presented that computes texture and color similarity among images. Content based image retrieval from large resources has become an area of wide interest now a days in many applications.  To speed up retrieval and similarity computation, the database images are analysed and the extracted regions are clustered according to their feature vectors. This process is performed offline before query processing, therefore to answer a query our system does not need to search the entire database images; instead just a number of candidate images are required to be searched for image similarity.

EFFICIENT CONTENT-BASED IMAGE RETRIEVAL USING FUZZY ORGANIZATION AND OPTIMAL RELEVANCE FEEDBACK

2003 ◽  
Vol 03 (01) ◽  
pp. 171-208 ◽  
Author(s):  
ANASTASIOS DOULAMIS ◽  
NIKOLAOS DOULAMIS ◽  
THEODORA VARVARIGOU
Keyword(s):  
Image Retrieval ◽  
Relevance Feedback ◽  
Visual Information ◽  
Information Needs ◽  
Cross Correlation ◽  
Feature Vector ◽  
Fuzzy Classification ◽  
Content Based Image Retrieval ◽  
Visual Content ◽  
Feature Vectors

The performance of a Content-Based Image Retrieval System (CBIR) depends on (a) the system's adaptability to the user's information needs, which permits different types of indexing and simultaneously reduces the subjectivity of human perception for the interpretation of the image visual content and (b) the efficient organization of the extracted descriptors, which represent the rich visual information. Both issues are addressed in this paper. Descriptor organization is performed using a fuzzy classification scheme fragmented into multidimensional classes, instead of the previous works where fuzzy histograms were created in one dimension using, for example, the feature vector norm. Multidimensionality relates the descriptors with one another and thus allows a compact and meaningful visual representation by mapping the elements of the resulted feature vectors with a physical visual interpretation. Furthermore, fuzzy classification is applied for all visual content descriptors, in contrast to the previous approaches where only color information is exploited. Two kinds of content descriptors are extracted in our case; global-based and region-based. The first refers to the global image characteristics, while the second exploits the region-based properties. Regions are obtained by applying a multiresolution implementation of the Recursive Shortest Spanning Tree (RSST) algorithm, called M-RSST in this paper. The second issue is addressed by proposing a computationally efficient relevance feedback mechanism based on an optimal weight updating strategy. The scheme relies on the cross-correlation measure, instead of the Euclidean distance which is mainly used in most relevance feedback algorithms. Cross-correlation is a normalized measure, which expresses how similar the two feature vectors are and thus it indicates a metric of their content similarity. The proposed scheme can be recursively implemented in the case of multiple feedback iterations, instead of the previous approaches. Furthermore, it provides reliable results regardless of the number of selected sample and the feature vector size improving relevance feedback performance, as compared to other approaches.

scholarly journals A comprehensive survey on content based image retrieval system and its application in medical domain

2018 ◽  
Vol 7 (2.31) ◽  
pp. 181
Author(s):  
K Srinivasa Reddy ◽  
R Anandan ◽  
K Kalaivani ◽  
P Swaminathan
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Visual Information ◽  
Medical Images ◽  
Content Based Image Retrieval ◽  
Radiology Department ◽  
Query Image ◽  
Feature Extraction Method ◽  
Comprehensive Survey ◽  
Medical Domain

Content Based Image Retrieval (CBIR) is an important and widely used technique for retrieval of different kinds of images from large database. Collection of information in database are available in different formats such as text, image, graph, chart etc. Here, our focus is on information which is available in the form of images. Searching and retrieval of the image from a large amount of database is difficult problem because it uses the image visual information such as shape, text and color for indexing and representation of an image. For efficient CBIR system, there is a need to develop different kinds of retrieval methods using feature extraction, similarity matching etc. Text Based Image Retrieval systems are used in many hospitals, but for large databases these are inefficient. To solve this problem, CBIR systems are proposed to retrieve matching images from database using automated feature extraction method. At present, medical imaging field finds extensive growth in the generation and evaluation of various types of medical images which are high inconsistency, usually fused and the combination of various minor composition structures. For easy retrieval, need to be development of feature extraction and image classification methods. Different methods are used for different kinds of medical images. The Radiology department and Cardiology department are the largest producers of medical images and the patient abnormal images can be stored with the normal images. CBIR uses query image as input and it retrieves the images, which are similar to the query more efficiently and effectively. This paper provides a comprehensive Survey about CBIR system and its one of the major application in medical domain.  

scholarly journals A Visual Saliency-Based Approach for Content-Based Image Retrieval

2021 ◽  
Vol 15 (1) ◽  
pp. 1-15
Author(s):  
Aamir Khan ◽  
Anand Singh Jalal
Keyword(s):  
Image Retrieval ◽  
Visual Information ◽  
Principal Component ◽  
Matrix Decomposition ◽  
Visual Saliency ◽  
Content Based Image Retrieval ◽  
Fast Recognition ◽  
Recognition Experiment ◽  
Semantic Concepts ◽  
High Level

During the past two decades an enormous amount of visual information has been generated; as a result, content-based image retrieval (CBIR) has received considerable attention. In CBIR the image is used as a query to find the most similar images. One of the biggest challenges in CBIR system is to fill up the “semantic gap,” which is the gap between low-level visual features and the high-level semantic concepts of an image. In this paper, the authors have proposed a saliency-based CBIR system that utilizes the semantic information of image and users search intention. In the proposed model, firstly a significant region is identified with the help of method structured matrix decomposition (SMD) using high-level priors that highlight the prominent area of the image. After that, a two-dimensional principal component analysis (2DPCA) is used as a feature, which is compact and effectively used for fast recognition. Experiment results are validated on different image dataset having an extensive collection of semantic classifications.

Recent Progress in Image and Video Segmentation for CBVIR

2011 ◽  
pp. 3224-3229 ◽  
Author(s):  
Yu-Jin Zhang
Keyword(s):  
Image Segmentation ◽  
General Theory ◽  
Visual Information ◽  
Recent Progress ◽  
Video Segmentation ◽  
Video Retrieval ◽  
Content Based Image Retrieval ◽  
Extended Version ◽  
Visual Information Retrieval ◽  
Simple Search

A simple search from EI Compendex by using the term “image segmentation” only in title field could produce around 5000 records (Zhang, 2006). However, as no general theory for image segmentation for different application domains, particular algorithms have been developed. The domain of Content-Based Image Retrieval (CBIR) is such a typical example, where many specific techniques have been proposed. An introduction focused on research works before 2004 can be found in Zhang (2005). This paper is an up-to-date and extended version from CBIR to CBVIR (Content-Based Visual Information Retrieval) by including CBVR (Content- Based Video Retrieval), which focused on the progress in last 3 years, and especially on video segmentation.

Content-Based Image Retrieval

2011 ◽  
pp. 744-749
Author(s):  
Alan Wee-Chung Liew ◽  
Ngai-Fong Law
Keyword(s):  
Image Retrieval ◽  
Visual Information ◽  
Image Features ◽  
User Preferences ◽  
Multimedia Systems ◽  
Content Based Image Retrieval ◽  
Top Down ◽  
Low Level ◽  
Feature Based ◽  
High Level

With the rapid growth of Internet and multimedia systems, the use of visual information has increased enormously, such that indexing and retrieval techniques have become important. Historically, images are usually manually annotated with metadata such as captions or keywords (Chang & Hsu, 1992). Image retrieval is then performed by searching images with similar keywords. However, the keywords used may differ from one person to another. Also, many keywords can be used for describing the same image. Consequently, retrieval results are often inconsistent and unreliable. Due to these limitations, there is a growing interest in content-based image retrieval (CBIR). These techniques extract meaningful information or features from an image so that images can be classified and retrieved automatically based on their contents. Existing image retrieval systems such as QBIC and Virage extract the so-called low-level features such as color, texture and shape from an image in the spatial domain for indexing. Low-level features sometimes fail to represent high level semantic image features as they are subjective and depend greatly upon user preferences. To bridge the gap, a top-down retrieval approach involving high level knowledge can complement these low-level features. This articles deals with various aspects of CBIR. This includes bottom-up feature- based image retrieval in both the spatial and compressed domains, as well as top-down task-based image retrieval using prior knowledge.

Content-Based Image Retrieval

2011 ◽  
pp. 212-216
Author(s):  
Timo R. Bretschneider ◽  
Odej Kao
Keyword(s):  
Science Education ◽  
Image Retrieval ◽  
Multimedia Information ◽  
Content Based Image Retrieval ◽  
Multimedia Technology ◽  
Human Beings ◽  
The Senses ◽  
Taste And Smell

Sensing and processing multimedia information is one of the basic traits of human beings: The audiovisual system registers and transports surrounding images and sounds. This complex re­cording system, complemented by the senses of touch, taste, and smell, enables perception and provides humans with data for analysing and interpreting the environment. Imitating this perception and the simulation of the processing was and still is one of the major leitmotifs of multimedia technology developments. The goal is to find a representation for every type of knowledge, which makes the reception and processing of information as easy as possible. The need to process given information, deliver it, and explain it to a certain audience exists in nearly all areas of day-to-day life: commerce, science, education, and entertainment (Smeulders, Worring, Santini, Gupta, & Jain, 2000).

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