Combining color and shape information for content-based image retrieval on the Internet

2003 ◽  
Author(s):  
Aristeidis Diplaros ◽  
Theo Gevers ◽  
Ioannis Patras
Keyword(s):  
Image Retrieval ◽  
Content Based Image Retrieval ◽  
The Internet ◽  
Shape Information

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.

FUZZY ART-BASED IMAGE CLUSTERING METHOD FOR CONTENT-BASED IMAGE RETRIEVAL

2007 ◽  
Vol 06 (02) ◽  
pp. 213-233 ◽  
Author(s):  
SANG-SUNG PARK ◽  
KWANG-KYU SEO ◽  
DONG-SIK JANG
Keyword(s):  
Image Retrieval ◽  
Content Based Image Retrieval ◽  
Image Clustering ◽  
Clustering Method ◽  
Image Region ◽  
Shape Information ◽  
Fuzzy Art ◽  
The Neural Network ◽  
Joint Histogram ◽  
Noise Input

In this paper, an image clustering method that is essential for content-based image retrieval in large image databases efficiently is proposed by color, texture, and shape contents. The dominant triple HSV (Hue, Saturation, and Value), which are extracted from quantized HSV joint histogram in the image region, are used for representing color information in the image. Entropy and maximum entry from co-occurrence matrices are used for texture information and edge angle histogram is used for representing shape information. Due to its algorithmic simplicity and the several merits that facilitate the implementation of the neural network, Fuzzy ART has been exploited for image clustering. Original Fuzzy ART suffers unnecessary increase of the number of output neurons when the noise input is presented. Therefore, the improved Fuzzy ART algorithm is proposed to resolve the problem by differently updating the committed node and uncommitted node, and checking the vigilance test again. To show the validity of the proposed algorithm, experimental results on image clustering performance and comparison with original Fuzzy ART are presented in terms of recall rates.

Comparison of Fourier and normal angle descriptors for the content-based image retrieval

2016 ◽  
Vol 83 (4) ◽  
Author(s):  
Pilar Hernández Mesa ◽  
Fernando Puente León
Keyword(s):  
Image Retrieval ◽  
Distance Functions ◽  
Content Based Image Retrieval ◽  
Fourier Descriptors ◽  
Shape Descriptors ◽  
Shape Information ◽  
Normal Vectors ◽  
Normal Angle

AbstractAppropriate methods are necessary to compare and search images automatically without using tags. In this work the retrieval of similar objects in images using the shape information is investigated. For this purpose, Fourier descriptors and the angles from the normal vectors along the boundaries of the objects are used as shape descriptors. Three different features are extracted from the Fourier descriptors to compare the objects. Three different distance functions are proposed to measure the similarity between the objects when the angles of the normal vectors are used as features. Finally, the appropriateness of these methods for the content-based image retrieval is compared at a commonly used database to test such cases.

scholarly journals CONTENT BASED IMAGE RETRIEVAL

2013 ◽  
pp. 294-299
Author(s):  
HARSHADA ANAND KHUTWAD ◽  
RAVINDRA JINADATTA VAIDYA
Keyword(s):  
Image Retrieval ◽  
Wavelet Decomposition ◽  
Texture Features ◽  
Content Based Image Retrieval ◽  
Image Search ◽  
Query Image ◽  
Shape Information ◽  
Color Correlogram ◽  
Similar Images ◽  
The Given

Content Based Image Retrieval is an interesting and most emerging field in the area of ‘Image Search’, finding similar images for the given query image from the image database. Current approaches include the use of color, texture and shape information. Considering these features in individual, most of the retrievals are poor in results and sometimes we are getting some non relevant images for the given query image. So, this dissertation proposes a method in which combination of color and texture features of the image is used to improve the retrieval results in terms of its accuracy. For color, color histogram based color correlogram technique and for texture wavelet decomposition technique is used. Color and texture based image

scholarly journals Image Retrieval Based on WBCH and Clustering Algorithm

2013 ◽  
Vol 5 (3) ◽  
pp. 604-613
Author(s):  
Asmita Bhaskar Shirsath ◽  
M. J. Chouhan ◽  
N. J Uke
Keyword(s):  
Image Retrieval ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Similarity Measures ◽  
Image Database ◽  
Content Based Image Retrieval ◽  
Shape Information ◽  
Image Descriptors ◽  
Image Retrieval System ◽  
Retrieval Result

Research on content-based image retrieval has gained tremendous momentum during the last decade. Color, texture and shape information have been the primitive image descriptors in content based image retrieval systems. In order to get faster  retrieval result from large-scale image database ,we proposed image retrieval system in which image database is first pre-processed by Wavelet Based Color Histogram (WBCH) and K-means algorithm and then using Hierarchical clustering algorithm we index the previous result and then by using similarity measures we retrieve the images from pre-processed database. Experiments show that this proposed method offers substantial increase in retrieval speed but needs to be improved on retrieval results.

Integrated color, texture and shape information for content-based image retrieval

2007 ◽  
Vol 10 (4) ◽  
pp. 333-343 ◽  
Author(s):  
Ryszard S. Choraś ◽  
Tomasz Andrysiak ◽  
Michał Choraś
Keyword(s):  
Image Retrieval ◽  
Content Based Image Retrieval ◽  
Shape Information ◽  
Color Texture
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