scholarly journals Unsupervised Selective Rank Fusion on Content-Based Image Retrieval

2019 ◽  
Author(s):  
Lucas Pascotti Valem ◽  
Daniel Carlos Guimarães Pedronette
Keyword(s):  
Image Retrieval ◽  
Visual Information ◽  
Learning Technologies ◽  
Content Based Image Retrieval ◽  
Visual Features ◽  
Machine Learning Methods ◽  
Retrieval Systems ◽  
Visual Descriptors ◽  
Promising Solution ◽  
Distinct Features

Mainly due to the evolution of technologies to store and share images, the growth of image collections have been remarkable for years. Therefore, developing effective methods to index and retrieve such extensive available visual information is indispensable. The CBIR (Content-Based Image Retrieval) systems are one of the main solutions for image retrieval tasks. These systems are mainly supported by the use of different visual descriptors and machine learning methods. Despite the relevant advances in the area, mainly driven by deep learning technologies, accurately computing the similarity between images remains a complex task in various scenarios due to the well known semantic gap problem. As distinct features produce complementary ranking results with different effectiveness performance, a promising solution consists in combining them. However, how to decide which visual features to combine is a very challenging task. This work proposes three novel methods for selecting and combining ranked lists by estimating their effectiveness in an unsupervised way. The approaches were evaluated in five different image collections and several descriptors, achieving results comparable or superior to the state-of-the-art in most of the evaluated scenarios.

scholarly journals Unsupervised Selective Rank Fusion for Content-based Image Retrieval

2020 ◽  
Author(s):  
Lucas Pascotti Valem ◽  
Daniel Carlos Guimarães Pedronette
Keyword(s):  
Machine Learning ◽  
Image Retrieval ◽  
State Of The Art ◽  
Training Data ◽  
Content Based Image Retrieval ◽  
Visual Features ◽  
Machine Learning Methods ◽  
Retrieval Systems ◽  
Promising Solution ◽  
Distinct Features

The CBIR (Content-Based Image Retrieval) systems are one of the main solutions for image retrieval tasks. These systems are mainly supported by the use of different visual features and machine learning methods. As distinct features produce complementary ranking results with different effectiveness performance, a promising solution consists in combining them. However, how to decide which visual features to combine is a very challenging task, especially when no training data is available. This work proposes three novel methods for selecting and combining ranked lists by estimating their effectiveness in an unsupervised way. The approaches were evaluated in five different image collections and several descriptors, achieving results comparable or superior to the state-of-the-art in most of the scenarios.

A Survey on Feature Based Image Retrieval Techniques

2013 ◽  
pp. 105-120
Author(s):  
Ling Shao
Keyword(s):  
Image Retrieval ◽  
State Of The Art ◽  
Content Based Image Retrieval ◽  
Visual Features ◽  
Retrieval Systems ◽  
Retrieval Algorithms ◽  
Invariant Regions ◽  
Feature Based ◽  
Recent Trends ◽  
Future Work

In this chapter, we review classical and state of the art Content-Based Image Retrieval algorithms. Techniques on representing and extracting visual features, such as color, shape, and texture, are first presented. Several well-known image retrieval systems using those features are also summarized. Then, two recent trends on image retrieval, namely semantic based methods and local invariant regions based methods, are discussed. We analyze the drawbacks of current approaches and propose directions for future work.

Enhanced Content-Based Image Retrieval Using Information Oriented Angle-Based Local Tri-Directional Weber Patterns

2021 ◽  
pp. 2150046
Author(s):  
Gangavarapu Venkata Satya Kumar ◽  
Pillutla Gopala Krishna Mohan
Keyword(s):  
Image Retrieval ◽  
Computer Applications ◽  
Content Based Image Retrieval ◽  
Visual Features ◽  
The Novel ◽  
Image Content ◽  
Query Image ◽  
Proposed Model ◽  
Maximum Mutual Information ◽  
Basic Features

In diverse computer applications, the analysis of image content plays a key role. This image content might be either textual (like text appearing in the images) or visual (like shape, color, texture). These two image contents consist of image’s basic features and therefore turn out to be as the major advantage for any of the implementation. Many of the art models are based on the visual search or annotated text for Content-Based Image Retrieval (CBIR) models. There is more demand toward multitasking, a new method needs to be introduced with the combination of both textual and visual features. This paper plans to develop the intelligent CBIR system for the collection of different benchmark texture datasets. Here, a new descriptor named Information Oriented Angle-based Local Tri-directional Weber Patterns (IOA-LTriWPs) is adopted. The pattern is operated not only based on tri-direction and eight neighborhood pixels but also based on four angles [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text]. Once the patterns concerning tri-direction, eight neighborhood pixels, and four angles are taken, the best patterns are selected based on maximum mutual information. Moreover, the histogram computation of the patterns provides the final feature vector, from which the new weighted feature extraction is performed. As a new contribution, the novel weight function is optimized by the Improved MVO on random basis (IMVO-RB), in such a way that the precision and recall of the retrieved image is high. Further, the proposed model has used the logarithmic similarity called Mean Square Logarithmic Error (MSLE) between the features of the query image and trained images for retrieving the concerned images. The analyses on diverse texture image datasets have validated the accuracy and efficiency of the developed pattern over existing.

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