Content Based Image Retrieval Using Active-Nets

2011 ◽  
pp. 85-114 ◽  
Author(s):  
David García Pérez ◽  
Antonio Mosquera ◽  
Stefano Berretti ◽  
Alberto Del Bimbo
Keyword(s):  
Image Retrieval ◽  
Structural Information ◽  
Similarity Measures ◽  
Research Area ◽  
Content Based Image Retrieval ◽  
Image Objects ◽  
Image Retrieval System ◽  
Deformable Structure ◽  
Retrieval Problem ◽  
Active Research

Content-based image retrieval has been an active research area in past years. Many different solutions have been proposed to improve performance of retrieval, but the large part of these works have focused on sub-parts of the retrieval problem, providing targeted solutions only for individual aspects (i.e., feature extraction, similarity measures, indexing, etc). In this chapter, we first shortly review some of the main practiced solutions for content-based image retrieval evidencing some of the main issues. Then, we propose an original approach for the extraction of relevant image objects and their matching for retrieval applications, and present a complete image retrieval system which uses this approach (including similarity measures and image indexing). In particular, image objects are represented by a two-dimensional deformable structure, referred to as “active net.” Active net is capable of adapting to relevant image regions according to chromatic and edge information. Extension of the active nets has been defined, which permits the nets to break themselves, thus increasing their capability to adapt to objects with complex topological structure. The resulting representation allows a joint description of color, shape, and structural information of extracted objects. A similarity measure between active nets has also been defined and used to combine the retrieval with an efficient indexing structure. The proposed system has been experimented on two large and publicly available objects databases, namely, the ETH-80 and the ALOI.

scholarly journals Combined global and local semantic feature–based image retrieval analysis with interactive feedback

2019 ◽  
Vol 53 (1-2) ◽  
pp. 3-17
Author(s):  
A Anandh ◽  
K Mala ◽  
R Suresh Babu
Keyword(s):  
Wavelet Transform ◽  
Image Retrieval ◽  
Research Work ◽  
Research Area ◽  
Content Based Image Retrieval ◽  
Image Retrieval System ◽  
False Image ◽  
Active Research ◽  
High Level ◽  
Interactive Feedback

Nowadays, user expects image retrieval systems using a large database as an active research area for the investigators. Generally, content-based image retrieval system retrieves the images based on the low-level features, high-level features, or the combination of both. Content-based image retrieval results can be improved by considering various features like directionality, contrast, coarseness, busyness, local binary pattern, and local tetra pattern with modified binary wavelet transform. In this research work, appropriate features are identified, applied and results are validated against existing systems. Modified binary wavelet transform is a modified form of binary wavelet transform and this methodology produced more similar retrieval images. The proposed system also combines the interactive feedback to retrieve the user expected results by addressing the issues of semantic gap. The quantitative evaluations such as average retrieval rate, false image acceptation ratio, and false image rejection ratio are evaluated to ensure the user expected results of the system. In addition to that, precision and recall are evaluated from the proposed system against the existing system results. When compared with the existing content-based image retrieval methods, the proposed approach provides better retrieval accuracy.

Approaches for Image Database Retrieval Based on Color, Texture, and Shape Features

2017 ◽  
pp. 28-50
Author(s):  
Kratika Arora ◽  
Ashwani Kumar Aggarwal
Keyword(s):  
Image Retrieval ◽  
Image Database ◽  
Extraction Methods ◽  
Research Area ◽  
Content Based Image Retrieval ◽  
Good Precision ◽  
Retrieval Systems ◽  
Main Motive ◽  
Image Retrieval System ◽  
Active Research

With an ever-increasing use and demand for digital imagery in the areas of medicine, sciences, and engineering, image retrieval is an active research area in image processing and pattern recognition. Content-based image retrieval (CBIR) is a method of finding images from a huge image database according to persons' interests. Content-based here means that the search involves analysis of the actual content present in the image. As the database of images is growing day by day, researchers/scholars are searching for better techniques for retrieval of images with good efficiency.This chapter first gives an overview of the various image retrieval systems. Then, the applications of CBIR in various fields and existing CBIR systems are described. The various image content descriptors and extraction methods are also explained. The main motive of the chapter is to study and compare the features that are used in Content Based Image Retrieval system and conclude on the system that retrieves images from a huge database with good precision and recall.

Fuzzy Techniques for Content-Based Image Retrieval

2018 ◽  
pp. 41-64
Author(s):  
Rose Bindu Joseph P. ◽  
Ezhilmaran Devarasan
Keyword(s):  
Image Retrieval ◽  
Fuzzy Theory ◽  
Spatial Relationship ◽  
Similarity Measures ◽  
Image Data ◽  
Content Based Image Retrieval ◽  
Human Reasoning ◽  
Fuzzy Distance ◽  
Image Retrieval System ◽  
Fuzzy Techniques

Content-based image retrieval aims to acquire images from huge databases by analyzing their visual features like color, texture, shape, and spatial relationship. The search for superior accuracy in image retrieval has resulted in concentrating more on semantic gap reduction between the low-level features and high level human reasoning. Fuzzy theory is a prevailing methodology which helps in attaining this goal by using attributes and interpretations similar to human reasoning. The vagueness and impreciseness in image data and the retrieval process can be modeled by fuzzy sets. This chapter analyses fuzzy theoretic approaches in various stages of content-based image retrieval system. Various fuzzy-based feature descriptors are discussed along with different fuzzy classification and indexing algorithms for content-based image retrieval. This chapter also presents an overview of various fuzzy distance and similarity measures for image retrieval. A novel fuzzy theoretic retrieval for finger vein biometric images is also proposed in this chapter with experiment and analysis.

Images Retrieval Based on Integrated Features

2014 ◽  
Vol 543-547 ◽  
pp. 2292-2295
Author(s):  
Ching Hung Su ◽  
Huang Sen Chiu ◽  
Mohd Helmy A. Wahab ◽  
Tsai Ming Hsiehb ◽  
You Chiuan Li ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
Texture Feature ◽  
Content Based Image Retrieval ◽  
Code Comparison ◽  
Retrieval Scheme ◽  
Image Retrieval System ◽  
Retrieval Problem ◽  
Color Sequence ◽  
Edge Histogram

We propose a practical image retrieval scheme to retrieve images efficiently. The proposed scheme transfers each image to a color sequence using straightforward 8 rules. Subsequently, using the color sequences to compare the images, namely color sequences comparison. We succeed in transferring the image retrieval problem to sequences comparison and subsequently using the color sequences comparison along with the texture feature of Edge Histogram Descriptor to compare the images of database. We succeed in transferring the image retrieval problem to quantized code comparison. Thus the computational complexity is decreased obviously. Our results illustrate it has virtues both of the content based image retrieval system and a text based image retrieval system.

scholarly journals AN OPTIMIZED FEATURE EXTRACTION TECHNIQUE FOR CONTENT BASED IMAGE RETRIEVAL

2013 ◽  
pp. 11-16
Author(s):  
SAVITHA SIVAN ◽  
THUSNAVIS BELLA MARY. I
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Feature Fusion ◽  
Texture Features ◽  
Research Area ◽  
Superior Performance ◽  
Content Based Image Retrieval ◽  
Multimedia Technologies ◽  
Active Research ◽  
Occurrence Matrix

Content-based image retrieval (CBIR) is an active research area with the development of multimedia technologies and has become a source of exact and fast retrieval. The aim of CBIR is to search and retrieve images from a large database and find out the best match for the given query. Accuracy and efficiency for high dimensional datasets with enormous number of samples is a challenging arena. In this paper, Content Based Image Retrieval using various features such as color, shape, texture is made and a comparison is made among them. The performance of the retrieval system is evaluated depending upon the features extracted from an image. The performance was evaluated using precision and recall rates. Haralick texture features were analyzed at 0 o, 45 o, 90 o, 180 o using gray level co-occurrence matrix. Color feature extraction was done using color moments. Structured features and multiple feature fusion are two main technologies to ensure the retrieval accuracy in the system. GIST is considered as one of the main structured features. It was experimentally observed that combination of these techniques yielded superior performance than individual features. The results for the most efficient combination of techniques have also been presented and optimized for each class of query.

scholarly journals Study of Content Based Image Retrieval Using Data Mining Techniques

2020 ◽  
pp. 397-403
Author(s):  
S. Dhinakaran
Keyword(s):  
Image Retrieval ◽  
Research Area ◽  
Research Field ◽  
Content Based Image Retrieval ◽  
High Dimensional ◽  
Query Image ◽  
Clustering Techniques ◽  
Clustering Technique ◽  
Active Research ◽  
And Performance

<p>The field of image retrieval has been an active research area for several decades and has been paid more and more attention in recent years as a result of the dramatic and fast increase in the volume of digital images. Content-based image retrieval (CBIR) is a new but widely adopted method for finding images from vast and un annotated image databases. In recent years, a variety of techniques have been developed to improve the performance of CBIR. In reaction to the needs of users, who feel problems connected with traditional methods of image searching and indexing, researchers focus their interest on techniques for retrieving images on the basis of automatically-derived features, often denoted as Content-Based Image Retrieval (CBIR). CBIR systems index the media documents using salient features extracted from the actual media rather than by textual annotations. Query by content is nowadays a very active research field, with many systems being developed by industrial and academic teams. Results performed by these teams are really promising. The situation gets diametrically different when we move our attention from the usual CBIR task, i.e. the retrieval of images which are similar (as a whole) to the query image, to the task “find all images that contain the query image”. The proposed CBIR technique uses more than one clustering techniques to improve the performance of CBIR. This optimized method makes use of K-means and Hierarchical clustering technique to improve the execution time and performance of image retrieval systems in high dimensional sets. In this similarity measure is totally based on colors. In this paper more focus area is the way of combination of clustering technique in order to get faster output of images. In this paper the clustering techniques are discussed and analyzed. Also, we propose a method HDK that uses more than one clustering technique to improve the performance of CBIR. This method makes use of hierarchical and divides and conquers K-means clustering technique with equivalency and compatible relation concepts to improve the performance of the K-Means for using in high dimensional datasets. It also introduced the feature like color, texture and shape for accurate and effective retrieval system.</p>

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.

scholarly journals A REVIEW ON CONTENT BASED IMAGE RETRIEVAL

2017 ◽  
Vol 15 (14) ◽  
pp. 7498-7503
Author(s):  
Nitika Seth ◽  
Sonika Jindal
Keyword(s):  
Image Processing ◽  
Image Retrieval ◽  
Retrieval System ◽  
Research Area ◽  
Content Based Image Retrieval ◽  
Future Research ◽  
Research Areas ◽  
Object Based ◽  
Image Retrieval System ◽  
Future Research Directions

Image retrieval means to recover the original image from the reconstructed image, here in this paper we have discussed latest techniques in the field of image retrieval for image processing. Content Based Image Retrieval (CBIR) is one of the most exciting and fastest growing research areas in the field of Image Processing. The techniques presented are Boosting image retrieval, soft query in image retrieval system, content based image retrieval by integration of metadata encoded multimedia features, and object based image retrieval and Bayesian image retrieval system. Some probable future research directions are also presented here to explore research area in the field of image retrieval

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