Image Features Extraction for Multimedia Database Content Description

Along with the progress of imaging modality and the wide utility of digital images (including video) in various fields, many potential content producers have emerged, and many image databases have been built. Because images require large amounts of storage space and processing time, how to quickly and efficiently access and manage these large, both in the sense of information contents and data volume, databases has become an urgent problem. The research solution for this problem, using content-based image retrieval (CBIR) techniques, was initiated in the last decade (Kato, 1992). An international standard for multimedia content descriptions, MPEG-7, was formed in 2001 (MPEG). With the advantages of comprehensive descriptions of image contents and consistence to human visual perception, research in this direction is considered as one of the hottest research points in the new century (Castelli, 2002; Zhang, 2003; Deb, 2004). Many practical retrieval systems have been developed; a survey of near 40 systems can be found in Veltkamp (2000). Most of them mainly use low-level image features, such as color, texture, and shape, etc., to represent image contents. However, there is a considerable difference between the users’ interest in reality and the image contents described by only using the above low-level image features. In other words, there is a wide gap between the image content description based on low-level features and that of human beings’ understanding. As a result, these low-level featurebased systems often lead to unsatisfying querying results in practical applications. To cope with this challenging task, many approaches have been proposed to represent and describe the content of images at a higher level, which should be more related to human beings’ understanding. Three broad categories could be classified: synthetic, semantic, and semiotic (Bimbo, 1999; Djeraba, 2002). From the understanding point of view, the semantic approach is natural. Human beings often describe image content in terms of objects, which can be defined at different abstraction levels. In this article, objects are considered not only as carrying semantic information in images, but also as suitable building blocks for further image understanding. The rest of the article is organized as follows: in “Background,” early object-based techniques will be briefly reviewed, and the current research on object-based techniques will be surveyed. In “Main Techniques,” a general paradigm for object-based image retrieval will be described; and different object-based techniques, such as techniques for extracting meaningful regions, for identifying objects, for matching semantics, and for conducting feedback are discussed. In “Future Trends,” some potential directions for further research are pointed out. In “Conclusion,” several final remarks are presented.

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Image features extraction of gas/liquid two-phase flow in horizontal pipeline by GLCM and GLGCM

2009 9th International Conference on Electronic Measurement & Instruments ◽

10.1109/icemi.2009.5274632 ◽

2009 ◽

Cited By ~ 8

Author(s):

Hongyi Wang ◽

Feng Dong

Keyword(s):

Two Phase Flow ◽

Image Features ◽

Features Extraction ◽

Phase Flow ◽

Two Phase

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IRIS IMAGE KEY POINTS DESCRIPTORS BASED ON PHASE CONGRUENCY

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w12-167-2019 ◽

2019 ◽

Vol XLII-2/W12 ◽

pp. 167-171 ◽

Cited By ~ 2

Author(s):

M. А. Protsenko ◽

E. A. Pavelyeva

Keyword(s):

Feature Vector ◽

Image Features ◽

Features Extraction ◽

New Method ◽

Phase Congruency ◽

Hermite Transform ◽

Iris Image ◽

Euclidean Metric ◽

Key Points ◽

Concentric Circles

<p><strong>Abstract.</strong> In this article the new method for iris image features extraction based on phase congruency is proposed. Iris image key points are calculated using the convolutions with Hermite transform functions. At each key point the feature vector characterizing this key point is obtained based on the phase congruency method. Iris key point descriptor contains phase congruency values at points located on concentric circles around the key point. To compare the key points, Euclidean metric between the key points descriptors is calculated. The distance between the iris images is equal to the number of matched iris key points. The proposed method was tested using the images from CASIA−IrisV4−Interval database and the value of EER&thinsp;=&thinsp;0.226% was obtained.</p>

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Analysis Methods Used to Extract Fingerprints Features

Traitement du signal ◽

10.18280/ts.380318 ◽

2021 ◽

Vol 38 (3) ◽

pp. 711-717

Author(s):

Mohammad S. Khrisat ◽

Rushdi. S. Abu Zneit ◽

Hatim Ghazi Zaini ◽

Ziad A. Alqadi

Keyword(s):

Detailed Comparison ◽

Image Features ◽

Features Extraction ◽

Color Images ◽

Comparison Analysis ◽

Image Rotation ◽

Analysis Methods ◽

The Stability

The fingerprint is used in many vital applications important to humans, which requires searching for an effective way to extract the characteristics of the fingerprint. In this paper we will study some of the most popular methods used to extract fingerprints features. For each method the efficiency, accuracy, flexibility and sensitivity for image rotation will be experimentally tested, measured, analyzed in order to give good recommendations of how and when to use a certain method of features extraction. A detailed comparison analysis between MLBP, K_means, WPT, Minutiae methods will be done using several color images in various rotation modes to insure the stability of image features.

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