An Efficient Content based Image Retrieval: CBIR (original) (raw)

Comparative study on Content-Based Image Retrieval (CBIR

The process of retrieving desired images from a large collection is widely used in applications of computer vision. In order to improve the retrieval performance an efficient and accurate system is required. Retrieving images based on the content i.e. color, texture, shape etc is called content based image retrieval (CBIR). The content is actually the feature of an image and is extracted through a meaningful way to construct a feature vector. Images having the least distance between their feature vectors are most similar. This paper gives comparison of three different approaches of CBIR based on image features and similarity measures taken for finding the similarity between two images. Results have shown that selecting an important image feature and calculating that through a meaningful way is of great importance in image retrieval. All the important features must be considered while constructing a feature vector and a proper similarity measure should be used for calculating the distance between two feature vectors. These parameters play very crucial role in deciding the overall performance of the any CBIR system. Some future direction were identified and under our future work.

REVIEW PAPER ON CONTENT BASED IMAGE RETRIEVAL FOR DIGITAL IMAGES

- Image retrieval is a very important area of digital image processing. Image can be retrieved from a large database on the basis of text, color, structure or content. Content-based image retrieval uses the visual contents of an image such as texture, color, shape, and spatial layout to represent and index the image. In typical CBIR systems, the visual content of the images in the database are extracted and described by multi-dimensional feature vectors .The feature vector of the images in the database for a feature database. To retrieve the images, users provide the retrieval system with example images. The system then changes these examples into its internal representation of feature vectors. In this paper we present the review on various content based image retrieval techniques.

Techniques of Content Based Image Retrieval : A Review

2017

The abstract should summarize the content of the paper. Try to keep the abstract below nowadays, computer vision and digital image processing are useful for content based image retrieval. Basically, computer vision systems try to retrieve an image to a user-defined specification or pattern (e.g., shape sketch, image color etc.). The goal of computer vision is to support image retrieval based on content properties like; shape, color, textures usually en coded in the form of feature vectors. Content based image retrieval (CBIR) considers the characteristics of the image itself, for example its shapes, colors and textures. In this study various techniques are used for feature extractions of CBIR images.

An Introduction of Content Based Image Retrieval Process

Image retrieval plays an important role in many areas like fashion, Engineering, Fashion, Medical, advertisement etc. As the process become increasingly powerful and memories become increasingly cheaper, the deployment of large image database for a variety of applications. It has now become realisable. Content Based image retrieval is method of extracting the similar image or matched image from the image database. It has become popular for getting image from the large image database. By using low level features like colour, texture, shape the retrieval become more efficient. There are many research algorithm developed for CBIR. In this paper we have used two retrieval process query by colour and query by texture. Colour features contain colour histogram in RGB colour space and texture features involve the invariant histogram and mean and standard deviation to retrieve the image. It is observed from the experiment that query by texture is more effective than colour for retrieving images. Precision and Recall provides the performance measurement.

CBIR: Content Based Image Retrieval

National Conference on Advances in Information Security (NCAIS-2010), 2010

The purpose of this report is to describe research and solution to the problem of designing a Content Based Image Retrieval, CBIR system. It outlines the problem, the proposed solution, the final solution and the accomplishments achieved. Due to the enormous increase in image database sizes, as well as its vast deployment in various applications, the need for CBIR development arose. Firstly, this report outlines a description of the primitive features of an image; texture, colour, and shape. These features are extracted and used as the basis for a similarity check between images. The algorithms used to calculate the similarity between extracted features, are then explained. Our final result was a MatLab built software application, with an image database, that utilized texture and colour features of the images in the database as the basis of comparison and retrieval. The structure of the final software application is illustrated. Furthermore, the results of its performance are illustrated by a detailed example. Keywords: Content Based Image Retrieval(CBIR), Similarity, Features and Image Database.

REVIEW OF CONTENT BASED IMAGE RETRIEVAL

Content-based visual information (CBVIR) or content-based image retrieval(CBIR) is one other important research areas in the field of computer vision. Many tools and programming have been developed to execute the queries based on the audio or visual content and that will help us to browse large multimedia repositories. A content-based image retrieval (CBIR) system is required to effectively and efficiently use information from these image repositories. This system helps the users to retrieve the relevant images from the database based on the content. In this paper we discuss various techniques for retrieving the images based on the contents of the image like color, shape and texture. Application areas where we used CBIR are numerous and diverse. We studied merits and demerits of various techniques of content based image retrieval.

Evaluation of Feature Extraction Techniques in Content Based Image Retrieval (CBIR) System

2013

The term CBIR refers to the process of retrieving similar images from a large collection of image database. The image retrieval is done on the basis of similarity matching between query image and database images. Different feature extraction techniques are being used to extract features of an image. The important image features are Color, Texture, Shape, Spatial location, edges etc. The CBIR (Content Based Image Retrieval) systems have achieved wide growth in past few years but still there are many limitations in CBIR systems yet to overcome. In this paper, we have evaluated feature extraction techniques based on color, texture and shape information and find that not a single feature vector is good for the retrieval parameters like precision and recall. There is the need of combining multiple feature vectors for increasing the effectiveness and efficiency of CBIR Systems.

A Study on Various Approaches of Content-Based Image Retrieval System

2019

Content-Based Image Retrieval is the field of digital image processing that has been used for the extraction of valuable information from huge datasets. In this process of CBIR, images have been extracted from huge datasets based on content available in the images. Various types of images are available under digital imaging. Different types of features have to be computed so that images can be extracted from the datasets on the basis of features. In this paper, various approaches have been discussed that has been used for the extraction of features based on content. Color, shape and texture based features have been extracted from the digital images so that relevant information available in the datasets can be extracted. This paper comprises review about various approaches of feature extraction from digital images. On the basis of review of these approaches, one can analyze best approach for feature extraction.

Evaluation of Feature Extraction Techniques in Content Based Image Retrieval (CBIR) System 1

2014

The term CBIR refers to the process of retrieving similar images from a large collection of image database. The image retrieval is done on the basis of similarity matching between query image and database images. Different feature extraction techniques are being used to extract features of an image. The important image features are Color, Texture, Shape, Spatial location, edges etc. The CBIR (Content Based Image Retrieval) systems have achieved wide growth in past few years but still there are many limitations in CBIR systems yet to overcome. In this paper, we have evaluated feature extraction techniques based on color, texture and shape information and find that not a single feature vector is good for the retrieval parameters like precision and recall. There is the need of combining multiple feature vectors for increasing the effectiveness and efficiency of CBIR Systems.

A Survey on Content Based Image Retrieval System

As the number of digital images increase a general TBIR system will not retrieve a large number of images depend on text. Content-based image retrieval has become one of the most active research areas in the past few years. This paper will give an overview of retrieving images from a large database. CBIR depends on various features like Low level or High level. The low level features include color, texture and shape. They are the visual feature to represent the image. The high level feature describes the concept of human brain. A Single feature can represent only a part of the image property. So multiple features are used to increase the efficiency of the image retrieval process. This paper will provide a survey on used color histogram, color mean, color structure descriptor and texture for feature extraction and how they are retrieved depending on their Euclidean distance.