Matching spatial relations using DB-tree for image retrieval

2002 ◽  
Author(s):  
Xiaobo Li ◽  
Xiaoqing Qu
Keyword(s):  
Image Retrieval ◽  
Spatial Relations

A Three Layered Approach for General Image Retrieval

2011 ◽  
pp. 78-83
Author(s):  
Richard Chbeir ◽  
Youssef Amghar ◽  
Andre Flory
Keyword(s):  
Image Retrieval ◽  
Knowledge Base ◽  
Three Dimensional ◽  
Spatial Relations ◽  
Application Domain ◽  
Global Approach ◽  
Retrieval Process ◽  
Retrieval Processes ◽  
Indexing And Retrieval ◽  
Layered Approach

Several approaches are proposed for retrieving images. Each of them describes image according to application domain requirements. No global approach exists to resolve retrieving image in complex domains (as medical one), in which content is multifaceted. A framework to retrieve medical images is presented. In this paper, we expose our three-dimensional approach applied to medical domain, and required elements for both knowledge base and retrieval process. The proposed approach, built on multifaceted aspect, offers all possibilities to describe image within multifaceted content (context, physical and semantic). Conceptual relations are presented for designing knowledge base for coherent and efficient indexing and retrieval processes. Required spatial relations of processes are also exposed.

Improving image retrieval by using spatial relations

2011 ◽  
Vol 62 (2) ◽  
pp. 479-505 ◽  
Author(s):  
Carlos Arturo Hernández-Gracidas ◽  
Luis Enrique Sucar ◽  
Manuel Montes-y-Gómez
Keyword(s):  
Image Retrieval ◽  
Spatial Relations

Emergence Index in Image Databases

2011 ◽  
pp. 1361-1365
Author(s):  
Sagarmay Deb
Keyword(s):  
Image Retrieval ◽  
Query Processing ◽  
Database Management ◽  
Image Representation ◽  
Spatial Relations ◽  
Image Databases ◽  
Multimedia Databases ◽  
Processing Algorithms ◽  
Image Representations ◽  
High Level

Images are generated everywhere from various sources. It could be satellite pictures, biomedical, scientific, entertainment, sports and many more, generated through video camera, ordinary camera, x-ray machine, and so on. These images are stored in image databases. Content-based image retrieval (CBIR) technique is being applied to access these vast volumes of images from databases efficiently. Some of the areas, where CBIR is applied, include weather forecasting, scientific database management, art galleries, law enforcement, and fashion design. Initially image representation was based on various attributes of the image like height, length, angle and was accessed using those attributes extracted manually and managed within the framework of conventional database management systems. Queries are specified using these attributes. This entails a high-level of image abstraction (Chen, Li & Wang, 2004). Also there was feature-based object-recognition approach where the process was automated to extract images based on color, shape, texture, and spatial relations among various objects of the image. Recently combining these two approaches, efficient image representation and query-processing algorithms, have been developed to access image databases. Recent CBIR research tries to combine both of these above mentioned approach and has given rise to efficient image representations and data models, query-processing algorithms, intelligent query interfaces and domain-independent system architecture. As we mentioned, image retrieval can be based on lowlevel visual features such as color (Antani, Rodney Long & Thoma, 2004; Deb & Kulkarni, 2007; Deb & Kulkarni, 2007a; Ritter & Cooper, 2007; Srisuk & Kurutach, 2002; Sural, Qian & Pramanik, 2002; Traina, Traina, Jr., Bueno, & Chino, 2003; Verma & Kulkarni, 2004), texture (Antani et al., 2004; Deb & Kulkarni, 2007a; Zhou, Feng & Shi, 2001), shape (Ritter & Cooper, 2007; Safar, Shahabi & Sun, 2000; Shahabi & Safar, 1999; Tao & Grosky, 1999), high-level semantics (Forsyth et al., 1996), or both (Zhao & Grosky, 2001). But most of the works done so far are based on the analysis of explicit meanings of images. But image has implicit meanings as well, which give more and different meanings than only explicit analysis provides. In this paper we provide the concepts of emergence index and analysis of the implicit meanings of the image which we believe should be taken into account in analysis of images of image or multimedia databases.

scholarly journals Content-based Image Retrieval for Map Georeferencing

2021 ◽  
Vol 4 ◽  
pp. 1-8
Author(s):  
Jonas Luft ◽  
Jochen Schiewe
Keyword(s):  
Image Retrieval ◽  
Information Access ◽  
Spatial Relations ◽  
Image Features ◽  
Content Based Image Retrieval ◽  
Mapping Data ◽  
Image Domain ◽  
Geographical Regions ◽  
Map Series ◽  
User Friendly

Abstract. In recent years, libraries have made great progress in digitising troves of historical maps with high-resolution scanners. Providing user-friendly information access for cultural heritage through spatial search and webGIS requires georeferencing of the hundreds of thousands of digitised maps.Georeferencing is usually done manually by finding “ground control points”, locations in the digital map image, whose identity is unambiguous and can easily be found in modern-day reference geodata/mapping data. To decide whether two symbols from different maps describe the same object, their semantic and spatial relations need to be matched. Automating this process is the only feasible way to georeference the immense quantities of maps in conceivable time. However, automated solutions for spatial matching quickly fail when faced with incomplete data – which is the greatest challenge when comparing maps of different ages or scales.These problems can be overcome by computing map similarity in the image domain. Treating maps as a special case of image processing allows efficient and robust matching and thus identification of geographical regions without the need to explicitly model semantics. We propose a method to encode worldwide reference VGI mapping data as image features, allowing the construction of an efficient lookup index. With this index, content-based image retrieval can be used for both geolocating a given map for georeferencing with high accuracy. We demonstrate our approach on hundreds of map sheets of different historical topographical survey map series, successfully georeferencing most of them within mere seconds.

Improved preservation of the otolithic membranes

1987 ◽  
Vol 45 ◽  
pp. 914-915
Author(s):  
G. M. Cohen ◽  
J. S. Grasso ◽  
M. L. Domeier ◽  
P. T. Mangonon
Keyword(s):  
Toluidine Blue ◽  
Potassium Phosphate ◽  
Vestibular Function ◽  
Spatial Relations ◽  
White Leghorn ◽  
Supporting Cells ◽  
Semithin Sections ◽  
Micromechanical Models ◽  
Soluble Components

Any explanation of vestibular micromechanics must include the roles of the otolithic and cupular membranes. However, micromechanical models of vestibular function have been hampered by unresolved questions about the microarchitectures of these membranes and their connections to stereocilia and supporting cells. Otolithic membranes are notoriously difficult to preserve because of severe shrinkage and loss of soluble components. We have empirically developed fixation procedures that reduce shrinkage artifacts and more accurately depict the spatial relations between the otolithic membranes and the ciliary bundles and supporting cells.We used White Leghorn chicks, ranging in age from newly hatched to one week. The inner ears were fixed for 3-24 h in 1.5-1.75% glutaraldehyde in 150 mM KCl, buffered with potassium phosphate, pH 7.3; when postfixed, it was for 30 min in 1% OsO4 alone or mixed with 1% K4Fe(CN)6. The otolithic organs (saccule, utricle, lagenar macula) were embedded in Araldite 502. Semithin sections (1 μ) were stained with toluidine blue.

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