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.

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 using Collaborative Color, Texture and Shape Features

2020 ◽  
Vol 9 (3) ◽  
pp. 1466-1469
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Texture Feature ◽  
Content Based Image Retrieval ◽  
Support Vector ◽  
Feature Extraction Method ◽  
Attribute Extraction ◽  
The Individual ◽  
Selection Of ◽  
Color Texture

Selection of feature extraction method is incredibly recondite task in Content Based Image Retrieval (CBIR). In this paper, CBIR is implemented using collaboration of color; texture and shape attribute to improve the feature discriminating property. The implementation is divided in to three steps such as preprocessing, features extraction, classification. We have proposed color histogram features for color feature extraction, Local Binary Pattern (LBP) for texture feature extraction, and Histogram of oriented gradients (HOG) for shape attribute extraction. For the classification support vector machine classifier is applied. Experimental results show that combination of all three features outperforms the individual feature or combination of two feature extraction techniques

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