The need for efficient content-based image retrieval system has increased hugely. Efficient and effective retrieval techniques of images are desired because of the explosive growth of digital images. Content based image retrieval (CBIR) is a promising approach because of its automatic indexing retrieval based on their semantic features and visual appearance. In this proposed system we investigate method for describing the contents of images which characterizes images by global descriptor attributes, where global features are extracted to make system more efficient by using color features which are color expectancy, color variance, skewness and texture feature correlation.
A Novel Method for Content Based Image Retrieval using Local Features and SVM...IRJET Journal
1) The document presents a novel approach for content-based image retrieval that uses local features like color, texture, and edges extracted from images.
2) It extracts these features and uses an SVM classifier to optimize retrieval results. This improves accuracy compared to other techniques that use only one content feature.
3) The proposed system is tested on parameters like accuracy, sensitivity, specificity, error rate, and retrieval time, and shows better performance than other methods.
This document provides a comprehensive review of recent developments in content-based image retrieval and feature extraction. It discusses various low-level visual features used for image retrieval, including color, texture, shape, and spatial features. It also reviews approaches that fuse low-level features and use local features. Machine learning and deep learning techniques for content-based image retrieval are also summarized. The document concludes by discussing open challenges and directions for future research in this area.
Applications of spatial features in cbir a surveycsandit
With advances in the computer technology and the World Wide Web there has been an
explosion in the amount and complexity of multimedia data that are generated, stored,
transmitted, analyzed, and accessed. In order to extract useful information from this huge
amount of data, many content based image retrieval (CBIR) systems have been developed in the
last decade. A typical CBIR system captures image features that represent image properties
such as color, texture, or shape of objects in the query image and try to retrieve images from the
database with similar features. Retrieval efficiency and accuracy are the important issues in
designing Content Based Image Retrieval System. The Shape and Spatial features are quiet easy
and simple to derive and effective. Researchers are moving towards finding spatial features and
the scope of implementing these features in to the image retrieval framework for reducing the
semantic gap. This Survey paper focuses on the detailed review of different methods and their
evaluation techniques used in the recent works based on spatial features in CBIR systems.
Finally, several recommendations for future research directions have been suggested based on
the recent technologies.
Precision face image retrieval by extracting the face features and comparing ...prjpublications
This document describes a proposed method for improving content-based face image retrieval. The method uses two orthogonal techniques: attribute-enhanced sparse coding and attribute-embedded inverted indexing. Attribute-enhanced sparse coding exploits global features to construct semantic codewords offline. Attribute-embedded inverted indexing considers local query image features in a binary signature to efficiently retrieve images. By combining these techniques, the method reduces errors and achieves better face image extraction from databases compared to existing content-based retrieval systems. It works by extracting features from the query image, matching them to database images, and returning ranked results.
This document presents a content-based image retrieval semantic model for shaped and unshaped objects. It proposes classifying objects into two categories: shaped objects with a fixed shape like animals and objects, and unshaped objects without a fixed shape like landscapes. For unshaped objects, local regions are classified by frequency of occurrence and semantic concepts are evaluated using color, shape, and regional dissimilarity factors. For shaped objects, semantic concepts are measured using normalized color, edge detection, particle removal, and shape similarity. Several existing content-based image retrieval techniques are also briefly discussed.
CBIR Processing Approach on Colored and Texture Images using KNN Classifier a...IRJET Journal
This document presents a content-based image retrieval system that uses color and texture features. It uses a K-nearest neighbor classifier to classify images based on color features and extract texture features using log-Gabor filters. Images are then ranked based on their similarity to the query image using Spearman's rank correlation coefficient. The system is tested on a dataset of flag images to retrieve the most similar flags to a given query image based on color and texture features. Experimental results show that the combined approach of using classification, similarity measures and log-Gabor filtering for color and texture features provides better retrieval performance than methods using only wavelets or Gabor filters.
This document describes a sketch-based image retrieval system that uses freehand sketches as queries to retrieve similar colored images from a database. The system first extracts features like color, texture, and shape from the sketch using descriptors such as Color and Edge Directivity Descriptor (CEDD) and Edge Histogram Descriptor (EHD). It then clusters the images in the database using k-means clustering based on the similarity of their features to the sketch. Finally, the system retrieves the most similar colored image from the clustered images as the output match for the user's sketch query.
A Comparative Study of Content Based Image Retrieval Trends and ApproachesCSCJournals
Content Based Image Retrieval (CBIR) is an important step in addressing image storage and management problems. Latest image technology improvements along with the Internet growth have led to a huge amount of digital multimedia during the recent decades. Various methods, algorithms and systems have been proposed to solve these problems. Such studies revealed the indexing and retrieval concepts, which have further evolved to Content-Based Image Retrieval. CBIR systems often analyze image content via the so-called low-level features for indexing and retrieval, such as color, texture and shape. In order to achieve significantly higher semantic performance, recent systems seek to combine low-level with high-level features that contain perceptual information for human. Purpose of this review is to identify the set of methods that have been used for CBR and also to discuss some of the key contributions in the current decade related to image retrieval and main challenges involved in the adaptation of existing image retrieval techniques to build useful systems that can handle real-world data. By making use of various CBIR approaches accurate, repeatable, quantitative data must be efficiently extracted in order to improve the retrieval accuracy of content-based image retrieval systems. In this paper, various approaches of CBIR and available algorithms are reviewed. Comparative results of various techniques are presented and their advantages, disadvantages and limitations are discussed.
A Novel Method for Content Based Image Retrieval using Local Features and SVM...IRJET Journal
1) The document presents a novel approach for content-based image retrieval that uses local features like color, texture, and edges extracted from images.
2) It extracts these features and uses an SVM classifier to optimize retrieval results. This improves accuracy compared to other techniques that use only one content feature.
3) The proposed system is tested on parameters like accuracy, sensitivity, specificity, error rate, and retrieval time, and shows better performance than other methods.
This document provides a comprehensive review of recent developments in content-based image retrieval and feature extraction. It discusses various low-level visual features used for image retrieval, including color, texture, shape, and spatial features. It also reviews approaches that fuse low-level features and use local features. Machine learning and deep learning techniques for content-based image retrieval are also summarized. The document concludes by discussing open challenges and directions for future research in this area.
Applications of spatial features in cbir a surveycsandit
With advances in the computer technology and the World Wide Web there has been an
explosion in the amount and complexity of multimedia data that are generated, stored,
transmitted, analyzed, and accessed. In order to extract useful information from this huge
amount of data, many content based image retrieval (CBIR) systems have been developed in the
last decade. A typical CBIR system captures image features that represent image properties
such as color, texture, or shape of objects in the query image and try to retrieve images from the
database with similar features. Retrieval efficiency and accuracy are the important issues in
designing Content Based Image Retrieval System. The Shape and Spatial features are quiet easy
and simple to derive and effective. Researchers are moving towards finding spatial features and
the scope of implementing these features in to the image retrieval framework for reducing the
semantic gap. This Survey paper focuses on the detailed review of different methods and their
evaluation techniques used in the recent works based on spatial features in CBIR systems.
Finally, several recommendations for future research directions have been suggested based on
the recent technologies.
Precision face image retrieval by extracting the face features and comparing ...prjpublications
This document describes a proposed method for improving content-based face image retrieval. The method uses two orthogonal techniques: attribute-enhanced sparse coding and attribute-embedded inverted indexing. Attribute-enhanced sparse coding exploits global features to construct semantic codewords offline. Attribute-embedded inverted indexing considers local query image features in a binary signature to efficiently retrieve images. By combining these techniques, the method reduces errors and achieves better face image extraction from databases compared to existing content-based retrieval systems. It works by extracting features from the query image, matching them to database images, and returning ranked results.
This document presents a content-based image retrieval semantic model for shaped and unshaped objects. It proposes classifying objects into two categories: shaped objects with a fixed shape like animals and objects, and unshaped objects without a fixed shape like landscapes. For unshaped objects, local regions are classified by frequency of occurrence and semantic concepts are evaluated using color, shape, and regional dissimilarity factors. For shaped objects, semantic concepts are measured using normalized color, edge detection, particle removal, and shape similarity. Several existing content-based image retrieval techniques are also briefly discussed.
CBIR Processing Approach on Colored and Texture Images using KNN Classifier a...IRJET Journal
This document presents a content-based image retrieval system that uses color and texture features. It uses a K-nearest neighbor classifier to classify images based on color features and extract texture features using log-Gabor filters. Images are then ranked based on their similarity to the query image using Spearman's rank correlation coefficient. The system is tested on a dataset of flag images to retrieve the most similar flags to a given query image based on color and texture features. Experimental results show that the combined approach of using classification, similarity measures and log-Gabor filtering for color and texture features provides better retrieval performance than methods using only wavelets or Gabor filters.
This document describes a sketch-based image retrieval system that uses freehand sketches as queries to retrieve similar colored images from a database. The system first extracts features like color, texture, and shape from the sketch using descriptors such as Color and Edge Directivity Descriptor (CEDD) and Edge Histogram Descriptor (EHD). It then clusters the images in the database using k-means clustering based on the similarity of their features to the sketch. Finally, the system retrieves the most similar colored image from the clustered images as the output match for the user's sketch query.
A Comparative Study of Content Based Image Retrieval Trends and ApproachesCSCJournals
Content Based Image Retrieval (CBIR) is an important step in addressing image storage and management problems. Latest image technology improvements along with the Internet growth have led to a huge amount of digital multimedia during the recent decades. Various methods, algorithms and systems have been proposed to solve these problems. Such studies revealed the indexing and retrieval concepts, which have further evolved to Content-Based Image Retrieval. CBIR systems often analyze image content via the so-called low-level features for indexing and retrieval, such as color, texture and shape. In order to achieve significantly higher semantic performance, recent systems seek to combine low-level with high-level features that contain perceptual information for human. Purpose of this review is to identify the set of methods that have been used for CBR and also to discuss some of the key contributions in the current decade related to image retrieval and main challenges involved in the adaptation of existing image retrieval techniques to build useful systems that can handle real-world data. By making use of various CBIR approaches accurate, repeatable, quantitative data must be efficiently extracted in order to improve the retrieval accuracy of content-based image retrieval systems. In this paper, various approaches of CBIR and available algorithms are reviewed. Comparative results of various techniques are presented and their advantages, disadvantages and limitations are discussed.
Content Based Image and Video Retrieval AlgorithmAkshit Bum
The document describes content-based image and video retrieval (CBIR) algorithms. It discusses how CBIR works by extracting features from query images, indexing images, and retrieving similar images based on color, shape, and texture features. CBIR techniques include reverse image search, semantic retrieval using queries, and relevance feedback to refine searches based on user input about retrieved images. The document provides examples of CBIR applications in areas like crime prevention, military, web searching, and medical diagnosis.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
This document outlines a presentation on content-based image retrieval (CBIR). It discusses the motivation for CBIR by describing limitations of text-based image retrieval, such as problems with image annotation, human perception, and queries that cannot be described with text. CBIR allows images to be retrieved based on automatically extracted visual features like color, texture, and histograms. A typical CBIR system extracts image features and then matches features to find visually similar images. Applications of CBIR include crime prevention, security, medical diagnosis, and intellectual property. The conclusion states that CBIR reduces computation time and increases user interaction compared to other methods.
C OMPARATIVE S TUDY OF D IMENSIONALITY R EDUCTION T ECHNIQUES U SING PCA AND ...csandit
The aim of this paper is to present a comparative s
tudy of two linear dimension reduction
methods namely PCA (Principal Component Analysis) a
nd LDA (Linear Discriminant Analysis).
The main idea of PCA is to transform the high dimen
sional input space onto the feature space
where the maximal variance is displayed. The featur
e selection in traditional LDA is obtained
by maximizing the difference between classes and mi
nimizing the distance within classes. PCA
finds the axes with maximum variance for the whole
data set where LDA tries to find the axes
for best class seperability. The proposed method is
experimented over a general image database
using Matlab. The performance of these systems has
been evaluated by Precision and Recall
measures. Experimental results show that PCA based
dimension reduction method gives the
better performance in terms of higher precision and
recall values with lesser computational
complexity than the LDA based method.
Low level features for image retrieval basedcaijjournal
In this paper, we present a novel approach for image retrieval based on extraction of low level features
using techniques such as Directional Binary Code (DBC), Haar Wavelet transform and Histogram of
Oriented Gradients (HOG). The DBC texture descriptor captures the spatial relationship between any pair
of neighbourhood pixels in a local region along a given direction, while Local Binary Patterns (LBP)
descriptor considers the relationship between a given pixel and its surrounding neighbours. Therefore,
DBC captures more spatial information than LBP and its variants, also it can extract more edge
information than LBP. Hence, we employ DBC technique in order to extract grey level texture features
(texture map) from each RGB channels individually and computed texture maps are further combined
which represents colour texture features (colour texture map) of an image. Then, we decomposed the
extracted colour texture map and original image using Haar wavelet transform. Finally, we encode the
shape and local features of wavelet transformed images using Histogram of Oriented Gradients (HOG) for
content based image retrieval. The performance of proposed method is compared with existing methods on
two databases such as Wang’s corel image and Caltech 256. The evaluation results show that our
approach outperforms the existing methods for image retrieval.
The document reviews various feature extraction techniques that have been used for content-based image retrieval (CBIR) systems. It discusses several approaches for extracting color, texture, shape and spatial features from images. It also examines different similarity measures and evaluation methods for CBIR systems, including precision, recall and distance metrics. Feature extraction is a key factor for CBIR, and the paper provides an overview of some of the major techniques that have been explored for this task.
This document provides a review of different techniques for image retrieval from large databases, including text-based image retrieval and content-based image retrieval (CBIR). CBIR uses visual features extracted from images like color, texture, and shape to search for similar images. The document discusses some limitations of CBIR and proposes video-based image retrieval as a new direction. It also surveys recent research in areas like feature extraction, indexing, and discusses future directions like reducing the semantic gap between low-level features and high-level meanings.
Literature Review on Content Based Image RetrievalUpekha Vandebona
This document summarizes a literature review on content-based image retrieval (CBIR). It discusses how CBIR uses computer vision techniques to automatically extract visual features from images for retrieval, unlike traditional concept-based methods that rely on metadata/text. The key visual features discussed are color, texture, and shape. A typical CBIR system architecture includes creating an image database, automatically extracting features, searching by example or semantics, and ranking results. Distance measures are used to compare image features and evaluate retrieval performance. Combining CBIR with concept-based techniques could improve image retrieval overall.
This document discusses content-based image mining techniques for image retrieval. It provides an overview of image mining, describing how image mining goes beyond content-based image retrieval by aiming to discover significant patterns in large image collections according to user queries. The document reviews several existing image mining techniques, including those using color histograms, texture analysis, clustering algorithms like k-means, and association rule mining. It discusses challenges in developing universal image retrieval methods and proposes combining low-level visual features with high-level semantic features. Overall, the document surveys the state of the art in content-based image mining and retrieval.
This document proposes a novel approach for detecting text in images and using the detected text as keywords to retrieve similar textual images from a database. The approach uses a text detection technique to find text regions in images, eliminates false positives, recognizes the text using OCR, and forms keywords using a neural language model. The detected keywords are then used to index and retrieve similar textual images from two benchmark datasets. Experimental results show the approach effectively retrieves similar textual images by exploiting the dominant text information in the images.
This dissertation discusses content-based image retrieval for medical imaging using texture features. The document outlines the background of CBIR and its applications in medical areas. It discusses using Gabor wavelet and gray level co-occurrence matrix (GLCM) texture features to extract features from medical images for retrieval. The methodology section describes extracting contrast, mean, standard deviation, entropy and energy features. Results show precision and recall rates for sample queries of knee, brain and chest images ranging from 79-88%. The conclusion discusses the proposed method's simplicity and speed while achieving average precision of 87.3%. The future scope discusses improving query time and updating the fuzzy rule base.
This document discusses content-based image retrieval (CBIR), which uses computer vision techniques to search for images based on their visual content rather than metadata. CBIR systems allow users to query image databases using either an example image or sketch. The system then analyzes features of the query image like color, texture, and shape to find visually similar images in the database. Users can provide relevance feedback to refine search results. CBIR has applications in domains like art collections, medical imaging, and scientific databases.
IRJET- Image based Information RetrievalIRJET Journal
This document discusses content-based image retrieval (CBIR) for retrieving images based on visual similarity. It focuses on using CBIR to match images of monuments for tourism applications. The paper describes extracting shape features using edge histogram descriptors to divide images into sub-images and compare edge distributions. An experiment matches images of Humayun's Tomb and the Statue of Liberty by comparing their edge magnitude values across sub-images. Similar edge distributions between two images' sub-images indicates similarity in shape and matches the images. The paper concludes CBIR using shape features can effectively match similar images of monuments to provide relevant information to users.
This document describes a proposed content-based image retrieval system using backpropagation neural networks (BPNN) and k-means clustering. It begins by discussing CBIR techniques and features like color, texture, and shape. It then outlines the proposed system which includes training a BPNN on image features, validating images, and testing by querying and retrieving similar images. Performance is analyzed based on metrics like accuracy, efficiency, and classification rate. Results show the system achieves up to 98% classification accuracy within 5-6 seconds.
An Unsupervised Cluster-based Image Retrieval Algorithm using Relevance FeedbackIJMIT JOURNAL
Content-based image retrieval (CBIR) systems utilize low level query image feature as identifying similarity between a query image and the image database. Image contents are plays significant role for image retrieval. There are three fundamental bases for content-based image retrieval, i.e. visual feature extraction, multidimensional indexing, and retrieval system design. Each image has three contents such as: color, texture and shape features. Color and texture both plays important image visual features used in Content-Based Image Retrieval to improve results. Color histogram and texture features have potential to retrieve similar images on the basis of their properties. As the feature extracted from a query is low level, it is extremely difficult for user to provide an appropriate example in based query. To overcome these problems and reach higher accuracy in CBIR system, providing user with relevance feedback is famous for provide promising solutio
This document discusses optimizing content-based image retrieval in peer-to-peer systems. It summarizes previous work on content-based image retrieval using multi-instance queries in peer-to-peer networks. The authors propose two optimizations to previous work: 1) clustering peers to reduce search time, and 2) constructing a search index at cluster heads to avoid searching each peer. Their experiments show the proposed approach reduces search time compared to previous work, with some reduction in accuracy that improves as more nodes are added. The authors plan future work to analyze performance with different cluster sizes and representations for faster search.
In this project, we proposed a Content Based Image Retrieval (CBIR) system which is used to retrieve a
relevant image from an outsized database. Textile images showed the way for the development of CBIR. It
establishes the efficient combination of color, shape and texture features. Here the textile image is given as
dataset. The images in database are loaded. The resultant image is given as input to feature extraction
technique which is transformation of input image into a set of features such as color, texture and shape.
The texture feature of an image is taken out by using Gray level co-occurrence matrix (GLCM). The color
feature of an image is obtained by HSI color space. The shape feature of an image is extorted by sobel
technique. These algorithms are used to calculate the similarity between extracted features. These features
are combined effectively so that the retrieval accuracy and recall rate is enhanced. The classification
techniques such as Support Vector Machine (SVM) are used to classify the features of a query image by
splitting the group such as color, shape and texture. Finally, the relevant images are retrieved from a large
database and hence the efficiency of an image is plotted.The software used is MATLAB 7.10 (matrix
laboratory) which is built software applications
Content based Image Retrieval from Forensic Image DatabasesIJERA Editor
Due to the proliferation of video and image data in digital form, Content based Image Retrieval has become a prominent research topic. In forensic sciences, digital data have been widely used such as criminal images, fingerprints, scene images and so on. Therefore, the arrangement of such large image data becomes a big issue such as how to get an interested image fast. There is a great need for developing an efficient technique for finding the images. In order to find an image, image has to be represented with certain features. Color, texture and shape are three important visual features of an image. Searching for images using color, texture and shape features has attracted much attention. There are many content based image retrieval techniques in the literature. This paper gives the overview of different existing methods used for content based image retrieval and also suggests an efficient image retrieval method for digital image database of criminal photos, using dynamic dominant color, texture and shape features of an image which will give an effective retrieval result.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Numerical simulation of Pressure Drop through a Compact Helical geometryIJERA Editor
Pipes are used in every industrial thermo-fluid equipment and systems, such as tubes, ducts, heat exchangers, air conditioning and refrigerating systems etc. Flatter velocity profiles and more uniform thermal environments are extremely desirous factors for improved performance of these flow reactors and heat exchangers. One means of achieving it in laminar flow systems is to use mixers and flow inverters. In the present study a new device is introduced by changing the dean number of fluid flowing in helically coiled tubes. The objective is to study velocity profile and pressure drop in the proposed device made up from the configurations of changing radius. Pressure drop in straight, helical coil and compact helical geometry configuration were compared using computational fluid dynamics software (FLUENT) results.
DIFUSI TEKNOLOGI PRODUKSI KONSENTRAT PROTEIN DARI IKAN GABUSbrawijaya university
Ringkasan dokumen ini adalah:
1. Dokumen ini membahas difusi teknologi produksi konsentrat protein dari ikan gabus sebagai suplemen gizi di Jayapura, Papua.
2. Teknologi yang didifusikan adalah produksi konsentrat protein ikan gabus skala komersial dan edukasi masyarakat tentang manfaat ikan gabus.
3. Hasilnya, satu UMKM di Jayapura telah mampu memproduksi 1000 kapsul konsentrat protein ikan gabus
Advocating against the privatization of education: the Chilean experiencePERIGlobal
This document discusses Chile's experience with privatizing education through a voucher system and increased private school participation. It summarizes key reforms from the 1980s, including decentralizing governance, deregulating teachers, introducing vouchers, and increasing private providers. The results section notes issues like asymmetric information, social segregation, and the importance of fees creating an unequal playing field. Social movements in 2006 and 2011 pushed for reclaiming the state's role and promoting social citizenship. The conclusion advocates for international advocacy using UN mechanisms to increase pressure for educational reforms in Chile.
Content Based Image and Video Retrieval AlgorithmAkshit Bum
The document describes content-based image and video retrieval (CBIR) algorithms. It discusses how CBIR works by extracting features from query images, indexing images, and retrieving similar images based on color, shape, and texture features. CBIR techniques include reverse image search, semantic retrieval using queries, and relevance feedback to refine searches based on user input about retrieved images. The document provides examples of CBIR applications in areas like crime prevention, military, web searching, and medical diagnosis.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
This document outlines a presentation on content-based image retrieval (CBIR). It discusses the motivation for CBIR by describing limitations of text-based image retrieval, such as problems with image annotation, human perception, and queries that cannot be described with text. CBIR allows images to be retrieved based on automatically extracted visual features like color, texture, and histograms. A typical CBIR system extracts image features and then matches features to find visually similar images. Applications of CBIR include crime prevention, security, medical diagnosis, and intellectual property. The conclusion states that CBIR reduces computation time and increases user interaction compared to other methods.
C OMPARATIVE S TUDY OF D IMENSIONALITY R EDUCTION T ECHNIQUES U SING PCA AND ...csandit
The aim of this paper is to present a comparative s
tudy of two linear dimension reduction
methods namely PCA (Principal Component Analysis) a
nd LDA (Linear Discriminant Analysis).
The main idea of PCA is to transform the high dimen
sional input space onto the feature space
where the maximal variance is displayed. The featur
e selection in traditional LDA is obtained
by maximizing the difference between classes and mi
nimizing the distance within classes. PCA
finds the axes with maximum variance for the whole
data set where LDA tries to find the axes
for best class seperability. The proposed method is
experimented over a general image database
using Matlab. The performance of these systems has
been evaluated by Precision and Recall
measures. Experimental results show that PCA based
dimension reduction method gives the
better performance in terms of higher precision and
recall values with lesser computational
complexity than the LDA based method.
Low level features for image retrieval basedcaijjournal
In this paper, we present a novel approach for image retrieval based on extraction of low level features
using techniques such as Directional Binary Code (DBC), Haar Wavelet transform and Histogram of
Oriented Gradients (HOG). The DBC texture descriptor captures the spatial relationship between any pair
of neighbourhood pixels in a local region along a given direction, while Local Binary Patterns (LBP)
descriptor considers the relationship between a given pixel and its surrounding neighbours. Therefore,
DBC captures more spatial information than LBP and its variants, also it can extract more edge
information than LBP. Hence, we employ DBC technique in order to extract grey level texture features
(texture map) from each RGB channels individually and computed texture maps are further combined
which represents colour texture features (colour texture map) of an image. Then, we decomposed the
extracted colour texture map and original image using Haar wavelet transform. Finally, we encode the
shape and local features of wavelet transformed images using Histogram of Oriented Gradients (HOG) for
content based image retrieval. The performance of proposed method is compared with existing methods on
two databases such as Wang’s corel image and Caltech 256. The evaluation results show that our
approach outperforms the existing methods for image retrieval.
The document reviews various feature extraction techniques that have been used for content-based image retrieval (CBIR) systems. It discusses several approaches for extracting color, texture, shape and spatial features from images. It also examines different similarity measures and evaluation methods for CBIR systems, including precision, recall and distance metrics. Feature extraction is a key factor for CBIR, and the paper provides an overview of some of the major techniques that have been explored for this task.
This document provides a review of different techniques for image retrieval from large databases, including text-based image retrieval and content-based image retrieval (CBIR). CBIR uses visual features extracted from images like color, texture, and shape to search for similar images. The document discusses some limitations of CBIR and proposes video-based image retrieval as a new direction. It also surveys recent research in areas like feature extraction, indexing, and discusses future directions like reducing the semantic gap between low-level features and high-level meanings.
Literature Review on Content Based Image RetrievalUpekha Vandebona
This document summarizes a literature review on content-based image retrieval (CBIR). It discusses how CBIR uses computer vision techniques to automatically extract visual features from images for retrieval, unlike traditional concept-based methods that rely on metadata/text. The key visual features discussed are color, texture, and shape. A typical CBIR system architecture includes creating an image database, automatically extracting features, searching by example or semantics, and ranking results. Distance measures are used to compare image features and evaluate retrieval performance. Combining CBIR with concept-based techniques could improve image retrieval overall.
This document discusses content-based image mining techniques for image retrieval. It provides an overview of image mining, describing how image mining goes beyond content-based image retrieval by aiming to discover significant patterns in large image collections according to user queries. The document reviews several existing image mining techniques, including those using color histograms, texture analysis, clustering algorithms like k-means, and association rule mining. It discusses challenges in developing universal image retrieval methods and proposes combining low-level visual features with high-level semantic features. Overall, the document surveys the state of the art in content-based image mining and retrieval.
This document proposes a novel approach for detecting text in images and using the detected text as keywords to retrieve similar textual images from a database. The approach uses a text detection technique to find text regions in images, eliminates false positives, recognizes the text using OCR, and forms keywords using a neural language model. The detected keywords are then used to index and retrieve similar textual images from two benchmark datasets. Experimental results show the approach effectively retrieves similar textual images by exploiting the dominant text information in the images.
This dissertation discusses content-based image retrieval for medical imaging using texture features. The document outlines the background of CBIR and its applications in medical areas. It discusses using Gabor wavelet and gray level co-occurrence matrix (GLCM) texture features to extract features from medical images for retrieval. The methodology section describes extracting contrast, mean, standard deviation, entropy and energy features. Results show precision and recall rates for sample queries of knee, brain and chest images ranging from 79-88%. The conclusion discusses the proposed method's simplicity and speed while achieving average precision of 87.3%. The future scope discusses improving query time and updating the fuzzy rule base.
This document discusses content-based image retrieval (CBIR), which uses computer vision techniques to search for images based on their visual content rather than metadata. CBIR systems allow users to query image databases using either an example image or sketch. The system then analyzes features of the query image like color, texture, and shape to find visually similar images in the database. Users can provide relevance feedback to refine search results. CBIR has applications in domains like art collections, medical imaging, and scientific databases.
IRJET- Image based Information RetrievalIRJET Journal
This document discusses content-based image retrieval (CBIR) for retrieving images based on visual similarity. It focuses on using CBIR to match images of monuments for tourism applications. The paper describes extracting shape features using edge histogram descriptors to divide images into sub-images and compare edge distributions. An experiment matches images of Humayun's Tomb and the Statue of Liberty by comparing their edge magnitude values across sub-images. Similar edge distributions between two images' sub-images indicates similarity in shape and matches the images. The paper concludes CBIR using shape features can effectively match similar images of monuments to provide relevant information to users.
This document describes a proposed content-based image retrieval system using backpropagation neural networks (BPNN) and k-means clustering. It begins by discussing CBIR techniques and features like color, texture, and shape. It then outlines the proposed system which includes training a BPNN on image features, validating images, and testing by querying and retrieving similar images. Performance is analyzed based on metrics like accuracy, efficiency, and classification rate. Results show the system achieves up to 98% classification accuracy within 5-6 seconds.
An Unsupervised Cluster-based Image Retrieval Algorithm using Relevance FeedbackIJMIT JOURNAL
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splitting the group such as color, shape and texture. Finally, the relevant images are retrieved from a large
database and hence the efficiency of an image is plotted.The software used is MATLAB 7.10 (matrix
laboratory) which is built software applications
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Survey on Multiple Query Content Based Image Retrieval SystemsCSCJournals
This paper reviews multiple query approaches for Content-Based Image Retrieval systems (MQIR). These are recently proposed Content-Based Image Retrieval systems that enhance the retrieval performance by conveying a richer understanding of the user high-level interest to the retrieval system. In fact, by allowing the user to express his interest using a set of query images, MQIR bridge the semantic gap with the low-level image features. Nevertheless, the main challenge of MQRI systems is how to compute the distances between the set of query images and each image in the database in a way that enhances the retrieval results and reflects the high-level semantic the user is interested in. For this matter, several approaches have been reported in the literature. In this paper, we investigate existing multiple query retrieval systems. We describe each approach, detail the way it computes the distances between the set of query images and each image in the database, and analyze its advantages and disadvantages in reflecting the high-level semantics meant by the user.
A Survey On: Content Based Image Retrieval Systems Using Clustering Technique...IJMIT JOURNAL
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Retrieval of Images Using Color, Shape and Texture Features Based on Contentrahulmonikasharma
The current study deals with deriving of image feature descriptor by error diffusion based block truncation coding (EDBTC). The image feature descriptor is basically comprised by the two error diffusion block truncation coding, color quantizers and its equivalent bitmap image. The bitmap image distinguish the image edges and textural information of two color quantizers to signify the color allocation and image contrast derived by the Bit Pattern Feature and Color Co-occurrence Feature. Tentative outcome reveal the benefit of proposed feature descriptor as contrast to existing schemes in image retrieval assignment under normal and textural images. The Error-Diffusion Block Truncation Coding method compresses an image efficiently, and at the same time, its consequent compacted information flow can provides an efficient feature descriptor intended for operating image recovery and categorization. As a result, the proposed design preserves an effective candidate for real-time image retrieval applications.
- Content-Based Image Retrieval (CBIR) is a technique used to retrieve images from large databases based on their visual content. It involves extracting features from an input query image and finding similar images from the database based on extracted features.
- The paper proposes a CBIR technique based on color feature extraction, where the queried image is divided into parts and color features are extracted to form a feature vector, which is then compared to feature vectors of images in the database to find similar images.
- The technique currently only uses color as the feature for similarity comparison, which limits its effectiveness, so future work involves combining multiple features like texture and shape for more accurate image retrieval.
This document discusses various techniques for image retrieval, including text-based, content-based, and hybrid approaches. Content-based image retrieval (CBIR) extracts visual features like color, texture, shape from images and is able to retrieve similar images to a query image. CBIR systems segment images, extract features, search databases, and return results. CBIR has advantages over text-based retrieval but challenges remain around the semantic gap between low-level features and high-level concepts. The document also discusses evaluating retrieval performance and promising future research directions like reducing the semantic gap.
This document discusses various techniques for image retrieval, including text-based, content-based, and hybrid approaches. Content-based image retrieval (CBIR) extracts visual features like color, texture, shape from images and is able to retrieve similar images to a query image. CBIR systems segment images, extract features, search databases, and return results. CBIR techniques are improving but challenges remain around reducing the semantic gap between low-level features and high-level concepts. Future areas of research include developing techniques more aligned with human perception and improving efficiency and interfaces.
Web Image Retrieval Using Visual Dictionaryijwscjournal
In this research, we have proposed semantic based image retrieval system to retrieve set of relevant images for the given query image from the Web. We have used global color space model and Dense SIFT feature extraction technique to generate visual dictionary using proposed quantization algorithm. The images are transformed into set of features. These features are used as inputs in our proposed Quantization algorithm for generating the code word to form visual dictionary. These codewords are used to represent images semantically to form visual labels using Bag-of-Features (BoF). The Histogram intersection method is used to measure the distance between input image and the set of images in the image database to retrieve similar images. The experimental results are evaluated over a collection of 1000 generic Web images to demonstrate the effectiveness of the proposed system.
Web Image Retrieval Using Visual Dictionaryijwscjournal
In this research, we have proposed semantic based image retrieval system to retrieve set of relevant images for the given query image from the Web. We have used global color space model and Dense SIFT feature extraction technique to generate visual dictionary using proposed quantization algorithm. The images are transformed into set of features. These features are used as inputs in our proposed Quantization algorithm for generating the code word to form visual dictionary. These codewords are used to represent images semantically to form visual labels using Bag-of-Features (BoF). The Histogram intersection method is used to measure the distance between input image and the set of images in the image database to retrieve similar images. The experimental results are evaluated over a collection of 1000 generic Web images to demonstrate the effectiveness of the proposed system.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
This document discusses content-based image mining techniques for image retrieval. It provides an overview of image mining, describing how image mining goes beyond content-based image retrieval by aiming to discover significant patterns in large image collections according to user queries. The document reviews several existing image mining techniques, including those using color histograms, texture analysis, clustering algorithms like k-means, and association rule mining. It discusses challenges in developing universal image retrieval methods and proposes combining low-level visual features with high-level semantic features. Overall, the document surveys the state of the art in content-based image mining and retrieval.
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Global Descriptor Attributes Based Content Based Image Retrieval of Query Images
1. Jaykrishna Joshi et al Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 5, Issue 2( Part 3), February 2015, pp.84-88
www.ijera.com 84 | P a g e
Global Descriptor Attributes Based Content Based Image
Retrieval of Query Images
Jaykrishna Joshi*, Dattatray Bade**
*(Department of Electronics and Telecommunication, Mumbai University, Mumbai-421 601)
** (Department of Electronics and Telecommunication, Mumbai University, Mumbai-400 037)
ABSTRACT
The need for efficient content-based image retrieval system has increased hugely. Efficient and effective
retrieval techniques of images are desired because of the explosive growth of digital images. Content based
image retrieval (CBIR) is a promising approach because of its automatic indexing retrieval based on their
semantic features and visual appearance. In this proposed system we investigate method for describing the
contents of images which characterizes images by global descriptor attributes, where global features are
extracted to make system more efficient by using color features which are color expectancy, color variance,
skewness and texture feature correlation.
Keywords - Content based image retrieval (CBIR), Retrieval, Query Image, Global Descriptor Attributes and
Color Histogram
I. INTRODUCTION
With the advances in computer technologies and
the advent of the World Wide Web, there has been an
explosion in the amount and complexity of digital
data being generated, stored, transmitted, analyzed,
and accessed. Much of this information is multimedia
in nature, including digital images, video, audio,
graphics, and text data[1]. In order to make use of
this vast amount of data, efficient and effective
techniques to retrieve multimedia information based
on its content need to be developed. Among the
various media types, images are of prime importance.
An image retrieval system is a computer system
for browsing, searching and retrieving images from a
large database of digital images. Most traditional and
common methods of image retrieval utilize some
method of adding metadata such as captioning,
keywords, or descriptions to the images so that
retrieval can be performed over the annotation words.
The reason behind research on multimedia systems
and content-based image retrieval (CBIR) is the fact
that multimedia databases deal with text, audio, video
and image data which could provide enormous
amount of information and which has affected life
style of human for the better.
CBIR is the application of computer vision to the
image retrieval problem, that is, the problem of
searching for digital images in large databases[2].
Content-based image retrieval also known as query
by image content (QBIC) and content-based visual
information retrieval (CBVIR). "Content-based"
means that the search will analyze the actual contents
of the image. The term 'content' in this context might
refer colors, shapes, textures, or any other
information that can be derived from the image itself.
There are some types of feature used for Image
retrieval such as color retrieval, textual retrieval,
shape retrieval and so on. Figure 1 show diagram
fundamental of content-based image retrieval system.
Figure.1 Block Diagram of Basic Content Based
Image Retrieval System
II. CONTENT-BASED IMAGE
RETRIEVAL (CBIR)
Content-based image retrieval (CBIR) has
become one of the most active research areas in the
past few years. Thus, many visual feature
representations have been explored and many CBIR
systems have been built. However, there are several
problems and challenges need to be consider in
attempt to apply CBIR systems. Firstly, the gap
between high-level semantic concept and low-level
visual features is great. In the CBIR context, an
image is represented by a set of low-level visual
features which are the features have no direct
correlation with high-level semantic concept[3].
Human prefer to retrieve images according to the
RESEARCH ARTICLE OPEN ACCESS
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“semantic” or “concept” of an image. But, CBIR
depends on the absolute distance of image features to
retrieve similar images Thereby, appear the gap
between high-level concepts and low-level features
which is the major difficulty that hinders further
development of CBIR systems. In other sentence, the
semantic gap problem is the lack of coincidence
between the image representation and the human
interpretation for an image.
There are many existing feature selection
techniques such as distribution based approaches,
Kullback-Leibler divergence (K-LD), boosting
manner, discriminant analysis (DA) method and
others. However, these feature selection techniques
remains a challenging problem for image retrieval. In
recent year, there are a lot of discriminant analysis
method had been proposed and used as a feature
selection method to improve relevance feedback.
These methods included multiple discriminant
analysis (MDA), biased discriminant analysis (BDA),
kernel-biased discriminant analysis (KBDA) and
nonparametric discriminant analysis (NDA). The
goal of discriminant analysis is to find a weight
matrix such that the distances between the two scatter
class matrixes are maximized.
However, these methods have their own
drawback that must be solved to improve the
performance of CBIR[4]. Basic single Gaussian
assumption which proposed by MDA and BDA
usually doesn‟t hold, since the few training samples
are always scattered in the high dimensional feature
space, and their effectiveness will be suffer.
Moreover, single Gaussian distribution means all
positive samples should be similar with similar view
angle and similar illumination, which are not the case
for CBIR. To overcome the problem of single
Gaussian distribution assumption, KBDA had been
introduced. But, this kernel based method has two
major drawbacks which is regularization approach is
often unstable and it is rely on parameter tuning.
Then, NDA had been proposed to solve the problem
in MDA, BDA and also KBDA. This approach can
only barely match the accuracy performance of
KBDA. As a conclusion, many feature selection
methods can not satisfy the requirements in CBIR
even though there are many method has been apply in
content-based image retrieval[5].
III. METHODOLOGY
The term Content-based image retrieval [CBIR]
describes the process of retrieving desired images
from a large collection on the basis of features (such
as colour, texture and shape) that can be
automatically extracted from the images
themselves.Content-based image retrieval, also
known as query by image content and content-based
visual information retrieval is the application of
computer vision to the image retrieval problem, that
is, the problem of searching for digital images in
large databases. „CONTENT BASED‟ means that the
search makes use of the contents of the images
themselves, rather than relying on human-input
metadata such as captions or keywords. The
surrounding world is composed of images.
There are different models for color image
representation. In the seventeen century Sir Isaac
Newton showed that a beam of sunlight passing
through a glass prism comes into view as a rainbow
of colors. Therefore, he first understood that white
light is composed of many colors[6]. Typically, the
computer screen can display 2^8 or 256 different
shades of gray. For color images this makes 2^ (3x8)
= 16,777,216 different colors. Clerk Maxwell showed
in the late nineteen century that every color image
cough be created using three images – Red, green and
Blue image. A mix of these three images can produce
every color. This model, named RGB model, is
primarily used in image representation. The RGB
image could be presented as a triple(R, G, B) where
usually R, G, and B take values in the range [0, 255].
Another color model is YIQ model (lamination (Y),
phase (I), quadrature phase (Q)). It is the base for the
color television standard. Images are presented in
computers as a matrix of pixels. They have finite
area. If we decrease the pixel dimension the pixel
brightness will become close to the real brightness. A
content-based image retrieval system (CBIR) is a
piece of software that implements CBIR. In CBIR,
each image that is stored in the database has its
features extracted and compared to the features of the
query image[7].
Searching and browsing image collections have
become important and active research fields in the
last decade. There are two approaches to image
retrieval: Text-Based approach and Content- Based
approach. Querying by image content is one of most
promising search techniques where users try to find
relevant images based on the given query image.
Color, texture, and shape are the low level features
that are usually preferred in content-based image
retrieval (CBIR) systems. Among these methods,
color histogram is the simplest, yet an effective visual
feature commonly used in color image retrieval[8].
The aim of query-by-color is to find images, whose
color features are similar to the color features of
query image. Although color histograms are
commonly used in computer vision and have the
computational advantages, it is a fact that they are
also very sensitive to small illumination changes and
quantization errors. In proposed method, firstly we
investigate two methods for describing the contents
of the images where first one characterizes images by
global descriptor attributes. Feature vectors based on
color and texture features are called Global
Descriptor Attributes[9].
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IV. DESIGN STEPS
Step-1: Feature Extraction:
First step in the proposed method is to extract the
image features to a distinguishable extinguishable
extent. Feature extraction is most critical stage. The
end result of feature extraction is a set of features
called feature vector; which constitutes the
representation of image (features such as color,
shape, texture etc. are used to describe the content of
image).
Global Descriptor Attributes:
1. Color
Color is the most extensively used visual content
for image retrieval. Color feature is one of the most
significant features of image retrieval. Its three-
dimensional values make its discrimination
potentiality superior to the single dimensional gray
values of images.
Extraction of color feature is done using
i. Color moment and
ii. Color Histogram
i. Color moment
Sticker and Orengo who propose the method of
color moment consider that the color information
focus on the low-level color moment of the image,
and they mainly do statistics for the first order,
second-order and third-order moment of each color
component. For image retrieval, the color moment is
a simple and effective representative method of color
features. Such color moment as first-order (mean)
and second (variance) and third-order (Skewness), is
proved to be very effective in presenting color
distribution of images. The three color moments are
defined with formulas as follows:
a) Color Expectancy: It is defined as the
average color.
Color Expectancy
b) Color Variance: It is defined as the
dispersion of color values from average.
Color
Variance
c) Color Skewness: It is defined as symmetry
of color distribution on the whole image.
Color
Skewness
Where N represents number of pixels in an image &
in an i j matrix represents the pixel value.
ii. Color Histogram:
An image histogram is a chart that shows the
distribution of intensities in an indexed or intensity
image. The CCH of an image indicates the frequency
of occurrence of every color in an image. The
approach more frequently adopted for CBIR systems
is based on the conventional color histogram (CCH),
which contains occurrences of each color obtained
counting all image pixel shaving that color.
2. Texture:
Texture is that innate property of all surfaces that
describes visual patters, and that contain important
information about the structural arrangement of the
surface and its relationship to the surrounding
environment. Texture is another important property
of images. Various texture representations have been
investigated in pattern recognition and computer
vision.
Step-2: Normalization:
If the image energy varies with position,
matching using cross correlation can fail. For
example, the correlation between the feature and an
exactly matching region in the image may be less
than the correlation between the feature and a bright
spot. The range of correlation is dependent on the
size of the feature. Normalization is performed so
that all images have a fixed dimension in order to
allow comparisons.
Step-3: Inputting Query Image:
To retrieve images, users provide the retrieval
system with example images or sketched figures
(Query image). The system then changes these
examples into its internal representation of feature
vectors.
Step-4: Matching:
The similarities /distances between the Global
Descriptor Attributes (GDA)of the query example or
sketch and those of the images in the database are
then calculated. This step involves matching these
Global Descriptor Attributes (GDA) to yield result
that are visually similar. If the Euclidean distance
between the query image and the images in the
database is small enough the corresponding image in
the database is considered as the match to the Query
image. Formula for Euclidean distance is given
below
Where
ED= Euclidean Distances
= Visual features of image query
= Visual features of images in database
i = Feature in which i start with i=1
Step-5: Simulation:
Instead of exact matching, content-based image
retrieval calculates visual similarities between a
query image and images in a database. Accordingly,
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the retrieval result is not a single image but a list of
images ranked by their similarities with the query
image. Many similarity measures have been
developed for image retrieval based on empirical
estimates of the distribution of features in recent
years. Different similarity/distance measures will
affect retrieval performances of an image retrieval
system significantly. In the proposed method this step
involves sorting the images based on their Euclidean
distance values and ranking them in ascending order.
After this calculate the Match Percentile (MP) by
using the formula
Match Percentile (MP) =
Where N= number of images in the database, R =
Rank of the image
Step-6: Applying Conventional Color
Histogram(CCH):
The CCH is constructed by counting the
number of pixels of each color. Histogram based
search method is investigated in RGB color space
only on filtered databases. A higher successful rate is
retrieving target image is obtained.
peppers.png dog.png guitar.png flower.png Gantrycrane
seq nos 1 2 3 4 5
balloon.png fabric.png pepper_1.png pepper_2.png pepper_3.png
seq nos 6 7 8 9 10
Figure 2: Results
Peppers.png Pepper_1.png Pepper_2.png Pepper_3.png Fabric.png
Figure 3: Top Five Sorted Images
V. RESULTS
The first image is the query image Peppers.png.
The eighth image which is Pepper_1 is rotated (180
degree) image of the query image. The ninth and the
tenth image Pepper_2 and Pepper_3 respectively are
developed by doing some changes in illumination and
brightness to the query image. These images are
included in the database to show and represent the
accuracy of the proposed algorithm.
Next step involves sorting of images in
descending order and extracting top five relevant
images (with respect to the given query image) from
the database based on their Average match percentile
value as shown in Figure 3.
Table.3 Histogram Error of top 5 sorted images
Rank Image name AMP Histogram
error
1 Peppers.png 1.0000 0
2 Pepper_1.png 0.8929 0.0425
3 Pepper_2.png 0.8810 0.0469
4 Pepper_3.png 0.8095 0.0451
5 Fabric.png 0.7857 0.0547
The Table.3 shows the top five extracted similar
images which are sorted with respect to the average
match percentile value. After applying CCH on the
five extracted images, histogram error is calculated
between the query image and the top five images. As
seen in the Table.3 above, the histogram error is zero
for the Peppers.png image which is the query image.
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A histogram error of zero indicates a perfect match.
Thus the retrieved image is same as the query image.
VI. CONCLUSION
The need to find a desired image from a
collection is shared by many professional groups,
including journalists, design engineers and art
historians. While the requirements of image users can
vary considerably, it can be useful to characterize
image queries into three levels of abstraction:
primitive features such as color or texture, logical
features such as the identity of objects shown and
abstract attributes such as the significance of the
scenes depicted. While CBIR systems currently
operate effectively only at the lowest of these levels,
most users demand higher levels of retrieval. Due to
the use of global descriptor values to extract images
the process of retrieval method is much faster than
the conventional approach of retrieval of images.
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