Techniques and applications of content-based image retrieval on the Internet

2005 ◽  
Author(s):  
Dingyuan Xia ◽  
Shuyu Liu
Keyword(s):  
Image Retrieval ◽  
Content Based Image Retrieval ◽  
The Internet

Machine Learning Approach for Content Based Image Retrieval

2012 ◽  
pp. 1-11 ◽  
Author(s):  
Siddhivinayak Kulkarni
Keyword(s):  
Neural Networks ◽  
Image Retrieval ◽  
Image Features ◽  
Content Based Image Retrieval ◽  
The Internet ◽  
Learning Approach ◽  
Query Image ◽  
Low Level ◽  
Machine Learning Approach ◽  
Efficient Retrieval

Developments in technology and the Internet have led to an increase in number of digital images and videos. Thousands of images are added to WWW every day. Content based Image Retrieval (CBIR) system typically consists of a query example image, given by the user as an input, from which low-level image features are extracted. These low level image features are used to find images in the database which are most similar to the query image and ranked according their similarity. This chapter evaluates various CBIR techniques based on fuzzy logic and neural networks and proposes a novel fuzzy approach to classify the colour images based on their content, to pose a query in terms of natural language and fuse the queries based on neural networks for fast and efficient retrieval. A number of experiments were conducted for classification, and retrieval of images on sets of images and promising results were obtained.

Performance Evaluation of Relevance Feedback for Image Retrieval by “Real-World” Multi-Tagged Image Datasets

2012 ◽  
Vol 3 (1) ◽  
pp. 1-16 ◽  
Author(s):  
Roberto Tronci ◽  
Luca Piras ◽  
Giorgio Giacinto
Keyword(s):  
Performance Evaluation ◽  
Image Retrieval ◽  
Performance Measures ◽  
Real World ◽  
Relevance Feedback ◽  
Real Life ◽  
Content Based Image Retrieval ◽  
The Internet ◽  
Feedback Mechanisms ◽  
Feedback Techniques

Anyone who has ever tried to describe a picture in words is aware that it is not an easy task to find a word, a concept, or a category that characterizes it completely. Most images in real life represent more than a concept; therefore, it is natural that images available to users over the Internet (e.g., FLICKR) are associated with multiple tags. By the term ‘tag’, the authors refer to a concept represented in the image. The purpose of this paper is to evaluate the performances of relevance feedback techniques in content-based image retrieval scenarios with multi-tag datasets, as typically performances are assessed on single-tag dataset. Thus, the authors show how relevance feedback mechanisms are able to adapt the search to user’s needs either in the case an image is used as an example for retrieving images each bearing different concepts, or the sample image is used to retrieve images containing the same set of concepts. In this paper, the authors also propose two novel performance measures aimed at comparing the accuracy of retrieval results when an image is used as a prototype for a number of different concepts.

Content Based Image Retrieval using Feature Extraction in JPEG Domain and Genetic Algorithm

2017 ◽  
Vol 7 (1) ◽  
pp. 226
Author(s):  
Parvin N ◽  
Kavitha P
Keyword(s):  
Remote Sensing ◽  
Genetic Algorithm ◽  
Image Retrieval ◽  
Weather Forecasting ◽  
Historical Research ◽  
Content Based Image Retrieval ◽  
The Internet ◽  
Compressed Domain ◽  
Security Systems ◽  
User Query

<p>Content-Based Image Retrieval (CBIR) aims at retrieving the images from the database based on the user query which is visual form rather than the traditional text form. The applications of CBIR extends from surveillance to remote sensing, medical imaging to weather forecasting, security systems to historical research and so on. Though extensive research is made on content based image retrieval in the spatial domain, we have most images in the internet which is JPEG compressed which pushes the need for image retrieval in the compressed domain itself rather than decoding it to raw format before comparison and retrieval. This research addresses the need to retrieve the images from the database based on the features extracted from the compressed domain along with the application of genetic algorithm in improving the retrieval results. The research focuses on various features and their levels of impact on improving the precision and recall parameters of the CBIR system. Our experimentation results also indicate that the CBIR features in compressed domain along with the genetic algorithm usage improves the results considerably when compared with the literature techniques.</p><p> </p>

scholarly journals Ekstraksi Fitur Warna, Tekstur dan Bentuk untuk Clustered-Based Retrieval of Images (CLUE)

2016 ◽  
Vol 16 (1) ◽  
pp. 85 ◽  
Author(s):  
I Gusti Rai Agung Sugiartha ◽  
Made Sudarma ◽  
I Made Oka Widyantara
Keyword(s):  
Image Retrieval ◽  
Texture Features ◽  
Image Texture ◽  
Content Based Image Retrieval ◽  
The Internet ◽  
Gray Level ◽  
Two Dimensional ◽  
Average Value ◽  
Shape Extraction ◽  
Internet Media

Picture (image) is a media that used for storing visual data, for example, two-dimensional images are often used to store an incident. Images on the internet media growth very rapidly. There are a lot of image, video, text or other content on the Internet. Image Index and image retrieval again become a topic of research in the last decade in which concentrated on how to get the meaning of an information contained in an image. Three methods outlined in the search for an image, the text-based image retrieval, content-based image retrieval and indexing images in the order of language. This study focuses on the preparation of the features of an image based on color and texture. Features colors using the average value of Hue image, texture features using Gray Level occurance Matrix (GLCM). Color, texture, and shape extraction technique resulted in eighteen (18) feature that can be used as features in the process of Clustering.DOI: 10.24843/MITE.1601.12

FIXED CONTENT BASED IMAGE RETRIEVAL TECHNIQUE

2017 ◽  
Vol 5 (3) ◽  
pp. 54
Author(s):  
MOHAMMED ILIAS SHAIK ◽  
CHAUHAN DINESH ◽  
ESAPALLI SRINIVAS ◽  
PADIGE VINEETH ◽  
◽  
...  
Keyword(s):  
Image Retrieval ◽  
Content Based Image Retrieval ◽  
Retrieval Technique
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