scholarly journals Image Content Description Using LSTM Approach

2017 ◽  
Author(s):  
Sonu Pratap Singh Gurjar ◽  
Shivam Gupta ◽  
Rajeev Srivastava
Keyword(s):  
Image Content ◽  
Content Description

Embedding spatial information into image content description for scene retrieval

2010 ◽  
Vol 43 (9) ◽  
pp. 3013-3024 ◽  
Author(s):  
N.V. Hoàng ◽  
V. Gouet-Brunet ◽  
M. Rukoz ◽  
M. Manouvrier
Keyword(s):  
Spatial Information ◽  
Image Content ◽  
Content Description

Advanced Techniques for Object-Based Image Retrieval

2011 ◽  
pp. 59-64
Author(s):  
Yu-Jin Zhang
Keyword(s):  
Image Retrieval ◽  
Image Features ◽  
Human Beings ◽  
Image Content ◽  
Semantic Approach ◽  
Practical Applications ◽  
Low Level ◽  
Content Description ◽  
Object Based ◽  
Data Volume

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.

scholarly journals Toward Content-Based Hyperspectral Remote Sensing Image Retrieval (CB-HRSIR): A Preliminary Study Based on Spectral Sensitivity Functions

2019 ◽  
Vol 11 (5) ◽  
pp. 600 ◽  
Author(s):  
Olfa Ben-Ahmed ◽  
Thierry Urruty ◽  
Noel Richard ◽  
Christine Fernandez-Maloigne
Keyword(s):  
Remote Sensing ◽  
Spectral Sensitivity ◽  
Hyperspectral Remote Sensing ◽  
Image Description ◽  
Image Content ◽  
Sensitivity Functions ◽  
Content Description ◽  
Spectral Bands ◽  
Physical Measurements ◽  
Imaging Sensors

With the emergence of huge volumes of high-resolution Hyperspectral Images (HSI)produced by different types of imaging sensors, analyzing and retrieving these images requireeffective image description and quantification techniques. Compared to remote sensing RGB images,HSI data contain hundreds of spectral bands (varying from the visible to the infrared ranges) allowingprofile materials and organisms that only hyperspectral sensors can provide. In this article, we studythe importance of spectral sensitivity functions in constructing discriminative representation ofhyperspectral images. The main goal of such representation is to improve image content recognitionby focusing the processing on only the most relevant spectral channels. The underlying hypothesisis that for a given category, the content of each image is better extracted through a specific set ofspectral sensitivity functions. Those spectral sensitivity functions are evaluated in a Content-BasedImage Retrieval (CBIR) framework. In this work, we propose a new HSI dataset for the remotesensing community, specifically designed for Hyperspectral remote sensing retrieval and classification.Exhaustive experiments have been conducted on this dataset and on a literature dataset. Obtainedretrieval results prove that the physical measurements and optical properties of the scene containedin the HSI contribute in an accurate image content description than the information provided by theRGB image presentation.

scholarly journals Combination of Texture, Color and Shape Operators to Describe Image Content: A Survey

2020 ◽  
Author(s):  
Sana K. Ardestani
Keyword(s):  
Texture Features ◽  
Local Distribution ◽  
Image Description ◽  
Image Content ◽  
Content Description ◽  
Combination Methods ◽  
Texture Characteristics ◽  
Computer Vision Applications ◽  
Shape Operators ◽  
Visual Properties

In many image processing and computer vision applications, the main aim is to describe image contents. So, different visual properties such as color, texture and shape are extracted to make aim. In this respect, texture information play important role in image description and visual pattern classification. Texture is referred to a specific local distribution of intensities that is repeated throughout the image. Since now different operations or descriptors have been proposed to analysis texture characteristics. In the multi object images specific texture operators usually doesn’t provide accurate results. So, in many cases, combination of texture operators are used to achieve more discriminant features. In this paper, some combination methods are survived to analysis effect of combinational texture features in image content description. Also, in the result part, different related methods are compared in terms of accuracy and computational complexity.

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