EFFICIENT CONTENT-BASED IMAGE RETRIEVAL USING FUZZY ORGANIZATION AND OPTIMAL RELEVANCE FEEDBACK

2003 ◽  
Vol 03 (01) ◽  
pp. 171-208 ◽  
Author(s):  
ANASTASIOS DOULAMIS ◽  
NIKOLAOS DOULAMIS ◽  
THEODORA VARVARIGOU
Keyword(s):  
Image Retrieval ◽  
Relevance Feedback ◽  
Visual Information ◽  
Information Needs ◽  
Cross Correlation ◽  
Feature Vector ◽  
Fuzzy Classification ◽  
Content Based Image Retrieval ◽  
Visual Content ◽  
Feature Vectors

The performance of a Content-Based Image Retrieval System (CBIR) depends on (a) the system's adaptability to the user's information needs, which permits different types of indexing and simultaneously reduces the subjectivity of human perception for the interpretation of the image visual content and (b) the efficient organization of the extracted descriptors, which represent the rich visual information. Both issues are addressed in this paper. Descriptor organization is performed using a fuzzy classification scheme fragmented into multidimensional classes, instead of the previous works where fuzzy histograms were created in one dimension using, for example, the feature vector norm. Multidimensionality relates the descriptors with one another and thus allows a compact and meaningful visual representation by mapping the elements of the resulted feature vectors with a physical visual interpretation. Furthermore, fuzzy classification is applied for all visual content descriptors, in contrast to the previous approaches where only color information is exploited. Two kinds of content descriptors are extracted in our case; global-based and region-based. The first refers to the global image characteristics, while the second exploits the region-based properties. Regions are obtained by applying a multiresolution implementation of the Recursive Shortest Spanning Tree (RSST) algorithm, called M-RSST in this paper. The second issue is addressed by proposing a computationally efficient relevance feedback mechanism based on an optimal weight updating strategy. The scheme relies on the cross-correlation measure, instead of the Euclidean distance which is mainly used in most relevance feedback algorithms. Cross-correlation is a normalized measure, which expresses how similar the two feature vectors are and thus it indicates a metric of their content similarity. The proposed scheme can be recursively implemented in the case of multiple feedback iterations, instead of the previous approaches. Furthermore, it provides reliable results regardless of the number of selected sample and the feature vector size improving relevance feedback performance, as compared to other approaches.

scholarly journals Content-Based Image Retrieval with Combined Features: Color and Gradient

2021 ◽  
Author(s):  
Mahmut Kılıçaslan ◽  
Recep Demirci
Keyword(s):  
Image Retrieval ◽  
Feature Vector ◽  
Color Image ◽  
Content Based Image Retrieval ◽  
Color Reduction ◽  
Feature Vectors ◽  
Image Histogram ◽  
Histogram Intersection ◽  
Combined Features ◽  
Combined Feature

Abstract Feature extraction is fundamental stage of effective content based image retrieval (CBIR). However, it remains challenging issue to extract low-level features for retrieval systems. This paper puts forward an effective solution proposal for the aforementioned problem. Initially, images and their gradients are clustered with multi-level thresholding. A codebook is generated with threshold values. The size of the codebook generated depends on the number of thresholds. Consequently, every pixel in color image is included in a cluster by means of the codebook. Color reduction is performed by assigning the average values of pixels in the same cluster. A cluster-based one-dimensional histogram (CBH) is created with the numbers of pixels in every cluster represented with a single color. Then the cluster-based feature vectors with histogram are extracted from original image and gradient image. Accordingly, relevant features are combined. The developed feature vector is called as combined feature vector (CFV). The most important advantages of CFV are that it performs an effective color reduction technique and feature presentation by processing texture information with gradient operator. Therefore, the main contribution of the combined feature vector suggested is its high accuracy and stability for image retrieval. The proposed method has been tested with Corel-1K, Corel-5K Corel-10K and GHIM-10K datasets. In addition, performances of different image histogram similarity techniques such as cosine, histogram intersection and Euclidean distance have been verified with the developed algorithm. Experimental results have been analyzed in two categories. Initially, CBIR results produced with combined feature vectors which are generated by Otsu, Kapur and center of gravity of histogram (CGH) procedures have been evaluated. Then, the CBIR strategy based on CGH method has been compared with CBIR systems with local binary pattern (LBP) and gradient-structures histogram (GSH). It was observed that CBIR approach based on CGH technique has significantly outperformed.

Content Analysis from User's Relevance Feedback for Content-Based Image Retrieval

2011 ◽  
pp. 216-234 ◽  
Author(s):  
Chia-Hung Wei ◽  
Chang-Tsun Li
Keyword(s):  
Machine Learning ◽  
Content Analysis ◽  
Image Retrieval ◽  
Relevance Feedback ◽  
Information Needs ◽  
Cultural Background ◽  
Content Based Image Retrieval ◽  
Interactive Search ◽  
Subjective Perceptions ◽  
The Individual

An image is a symbolic representation; people interpret an image and associate semantics with it based on their subjective perceptions, which involves the user’s knowledge, cultural background, personal feelings and so on. Content-based image retrieval (CBIR) systems must be able to interact with users and discover the current user’s information needs. An interactive search paradigm that has been developed for image retrieval is machine learning with a user-in-the-loop, guided by relevance feedback, which refers to the notion of relevance of the individual image based on the current user’s subjective judgment. Relevance feedback serves as an information carrier to convey the user’s information needs / preferences to the retrieval system. This chapter not only provides the fundamentals of CBIR systems and relevance feedback for understanding and incorporating relevance feedback into CBIR systems, but also discusses several approaches to analyzing and learning relevance feedback.

A Bayesian Image Retrieval Framework

2012 ◽  
pp. 143-159
Author(s):  
Rui Zhang ◽  
Ling Guan
Keyword(s):  
Image Retrieval ◽  
Relevance Feedback ◽  
Visual Information ◽  
General Framework ◽  
Evaluation Criteria ◽  
Semantic Gap ◽  
Visual Content ◽  
Multiple Sources ◽  
Visual Appearance ◽  
Feedback Techniques

Conventional approaches to content-based image retrieval exploit low-level visual information to represent images and relevance feedback techniques to incorporate human knowledge into the retrieval process, which can only alleviate the semantic gap to some extent. To further boost the performance, a Bayesian framework is proposed in which information independent of the visual content of images is utilized and integrated with the visual information. Two particular instances of the general framework are studied. First, context which is the statistical relation across the images is integrated with visual content such that the framework can extract information from both the images and past retrieval results. Second, characteristic sounds made by different objects are utilized along with their visual appearance. Based on various performance evaluation criteria, the proposed framework is evaluated using two databases for the two examples, respectively. The results demonstrate the advantage of the integration of information from multiple sources.

scholarly journals A Review On Content Based Image Retrieval

2018 ◽  
Vol 17 (2) ◽  
pp. 7226-7235
Author(s):  
Bohar Singh ◽  
Mrs.Mehak Aggarwal
Keyword(s):  
Image Retrieval ◽  
Visual Information ◽  
Image Database ◽  
Image Databases ◽  
Content Based Image Retrieval ◽  
Visual Images ◽  
Large Database ◽  
Feature Vectors ◽  
Digital Storage ◽  
Similarity Computation

In current years, very huge collections of images and videos have grown swiftly. In parallel with this boom, content-based image retrieval and querying the indexed collections of images from the large database are required to access visible facts and visual information. Three of the principle additives of the visual images are texture, shape and color. Content based image retrieval from big sources has a wide scope in many application areas and software’s.  To accelerate retrieval and similarity computation, the database images are analyzed and the extracted regions are clustered or grouped together with their characteristic feature vectors. As a result of latest improvements in digital storage technology, it's easy and possible to create and store the large quantity of images inside the image database.  These collections may additionally comprise thousands and thousands of images and terabytes of visual information like their shape, texture and color.  For users to make the most from those image databases, efficient techniques and mechanisms of searching should be devised. Having a computer to do the indexing primarily based on a CBIR scheme attempts to deal with the shortcomings of human-based indexing.  Since anautomated process on a computer can analyze and process the images at a very quick and efficient rate that human can never do alone. In this paper, we will discuss the structure of CBIR with their feature vectors.

scholarly journals COMPREHENSIVE STUDY ON CONTENT BASED IMAGE RETRIEVAL WITH THEIR FEATURES

2017 ◽  
Vol 16 (6) ◽  
pp. 6962-6967
Author(s):  
Pritpal Kaur ◽  
Sukhvir Kaur
Keyword(s):  
Image Retrieval ◽  
Visual Information ◽  
Image Database ◽  
Content Based Image Retrieval ◽  
Visual Images ◽  
Large Database ◽  
Feature Vectors ◽  
Digital Storage ◽  
Similarity Computation ◽  
Comprehensive Study

In current years, very huge collections of images and videos have grown swiftly. In parallel with this boom, content-based image retrieval and querying the indexed collections of images from the large database are required to access visible facts and visual information. Three of the principle additives of the visual images are texture, shape and color. Content based image retrieval from big sources has a wide scope in many application areas and software’s. To accelerate retrieval and similarity computation, the database images are analyzed and the extracted regions are clustered or grouped together with their characteristic feature vectors. As a result of latest improvements in digital storage technology, it's easy and possible to create and store the large quantity of images inside the image database. These collections may additionally comprise thousands and thousands of images and terabytes of visual information like their shape, texture and color. For users to make the most from those image databases, efficient techniques and mechanisms of searching should be devised. Having a computer to do the indexing primarily based on a CBIR scheme attempts to deal with the shortcomings of human-based indexing. Since an automated process on a computer can analyze and process the images at a very quick and efficient rate that human can never do alone. In this paper, we will discuss the structure of CBIR with their feature vectors.

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