scholarly journals Performance Evaluation of Color Descriptors under Illumination Variation

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Danyu Sun ◽  
Xixuan Zhao ◽  
Jiangming Kan
Keyword(s):  
Image Retrieval ◽  
Visual Information ◽  
Theoretical Basis ◽  
Recognition Rate ◽  
Evaluation Criteria ◽  
Surface Characteristics ◽  
Illumination Variation ◽  
Color Descriptors ◽  
Different Color ◽  
Indoor And Outdoor

Color descriptors, which involve the extraction of color information that is robust to illumination variation, are indispensable for accessing reliable visual information as illumination variation is inevitable in many practical cases. There has been many color descriptors proposed in literature, but the performance of different color descriptors in different scenes under illumination variation and the influence of the surface characteristics have not been investigated. In this paper, we first systematically introduced the theoretical basis of color descriptors, categorized the existing color descriptors according to the theoretical basis, and then compared the performance of different color descriptors utilized for image recognition and image retrieval tasks on both the indoor and outdoor image datasets. We adopted the recognition rate and normalized average rank as the evaluation criteria to measure the performance of color descriptors. Experiment results show that the color moment invariants (CMI) provide the optimal balance between the performance and dimensions in most tests, and color descriptors derived from physical reflectance models are more suitable for object recognition and image retrieval. We also concluded the best color descriptors for each kind of scene and surface characteristics.

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 Color Based Image Retrieval by Combining Various Features

2019 ◽  
Vol 9 (2) ◽  
pp. 454-460
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
Visual Features ◽  
Semantic Features ◽  
Retrieval Rate ◽  
Image Retrieval System ◽  
Mathematical Formulas ◽  
Image Set ◽  
Different Color ◽  
Image Collection

Content based image retrieval system retrieve the images according to the strong feature related to desire as color, texture and shape of an image. Although visual features cannot be completely determined by semantic features, but still semantic features can be integrate easily into mathematical formulas. This paper is focused on retrieval of images within a large image collection, based on color projection by applying segmentation and quantification on different color models and compared for good result. This method is applied on different categories of image set and evaluated its retrieval rate in different models

Retrieval of Multimedia Information Using Content-Based Image Retrieval (CBIR) Techniques

Biometrics ◽  
2017 ◽  
pp. 652-689
Author(s):  
Anupam Mukherjee
Keyword(s):  
Image Retrieval ◽  
Multimedia Information ◽  
Visual Information ◽  
Video Retrieval ◽  
Automatic Generation ◽  
Content Based Image Retrieval ◽  
Manual Labor ◽  
Multidimensional Indexing ◽  
Other Information ◽  
Human Operators

This chapter will focus on the concept of Content-based image retrieval. Searching of an image or video database based on text based description is a manual labor intensive process. Descriptions of the file are usually typed manually for each image by human operators because the automatic generation of keywords for the images is difficult without incorporation of visual information and feature extraction. This method is impractical in today's multimedia information era. “Content-based” means that the search will analyze the actual contents of the image rather than the metadata such as keywords, tags, and descriptions associated with the image. The term “content” in this context might refer to colors, shapes, textures, or any other information that can be derived from the image itself. Several important sections are highlighted in this chapter, like architectures, query techniques, multidimensional indexing, video retrieval and different application sections of CBIR.

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