1 Image Databases INLS 623 Lina Huang Elise Moore Ketan Palshikar Nevin Yang
2 What is an image database? Definition - a collection of image data, typically associated with the activities of one or more related organizations Focuses on the organization of images and its metadata in an efficient manner Sometimes delves more thoroughly into an image's content query by an image's characteristic rather than just keywords/tags Consumer, professional, and industry software available
3 Characteristics of Image DB Efficiently store images in database Data modeling: An image data model must be developed to deal with the issues of representing image in database. Support image manipulation in database level: crop resize etc Store metadata to image
4 Why would you want an image database? Manageability Images stored in the database can be directly linked with metadata Security Fine grained security is possible. Access to an image can be restricted to individual users and it also offers other restriction controls. Backup/Recovery Backing up the database will backup every image so it simplifies the process. Only one recovery procedure needed in an event of failure Extensibility An image can be converted from one format to another. Metadata can be extracted from it. It can be copied, resized and the image quality controlled Flexibility Sets of images can be deleted, updated or copied as easy as it is to write a query. Images can be linked together and metadata can be easily attached to them. All data related to an image or set of images can logically co-exist
5 Standards Content-Based Image Retrieval (CBIR) - addresses problem of searching for digital images in large databases The search analyzes actual contents of the image (colors, shapes, textures, etc derived from image itself). This eliminates the dependency on metadata such as captions/keywords. SQL/MM Still Image Part of SQL/MM Standard Multiple formats: JPEG, TIFF, GIF, etc Can scale, crop, or rotate an image as well as create a thumbnail Image-specific functions to search by image content rather than its metadata SI_AverageColor imagine wanting to find an image primarily green to be used in advertising outdoor furniture
7 Standards Needed File formats controversy Camera makers use proprietary file formats which causes problems when trying to develop a standard Vast number of file formats - JPEG, TIFF, GIF, BMP, etc. Hard to just use one as a standard because multiple formats are commonly used. Storing thumbnails vs. dynamically creating thumbnails Storing can quickly view images without calling the original (often large) file at the cost of storage size and data redundancy. Creating thumbnails at middle tier will avoid making a thumbnail for every image and only dynamically create them when required at the cost of performance.
8 Images in Popular Database Solutions Databases with image support: Oracle MySQL SQL Server Access 2007 These solutions offer some basic image support (BLOB), but merely as an image collection with metadata attached to images. It does not allow for query based on image content. The above-mentioned popular databases encourage users to only have links in the database to images and not host the image itself. The exception is Oracle and its 10g/11g databases.
9 Image Storage Object Relational Database Traditional relational database: table is the first form, and attributes stored in table are atomic.image is usally saved as external file. Object relational database: allow users to define additional data types while remaining the relational model.(class,inheritance,polymorphism.) User-defined Abstract Data Type create type ImageInfoType( date varchar(12), location_latitude real, location_longitude real) // define an image data type, to store location information create table locate_photos(photo_id integer, photographer varchar(20), photo blob not null, photo_location ImageInfoType) //define a table to store photos, the type of photo_location is ImageInfoType. Binary Large Object Image and video are physically stored as large binary data Stored differently from other column value However it is still manipulated as a single atomic field in a table record.
10 Image Retrieval and Manipulate User-defined functions User-defined functions are equivalent to methods in the objectoriented context. It is used to define image operations on those userdefined data types. DB2 and Oracle even support user-defined functions written in C or Java programming languages. Extended SQL Query ORDBMSs extend the basic SQL language to allows UDFs to be called directly within SQL querie. select photographer, contvert_to_grayscale(photo) from locate_photos where within_distance(photo_location, '1', '30.45, ');
11 Image Operation Import and export image Convert image format Scale image to different resolution Change compression type, black-white, gray-scale image inversion, etc. Content-based search: search by features, its distribution of color, people included in the image, taken location, device, etc.
12 current indexing techniques EXIF - file information IPTC - image metadata IPTCforXMP - extension of metadata IPTC G2-standards (XML)
14 future indexing techniques Query by example Semantic retrieval Content comparison techniques: Color Texture Shape
15 Trade offs Database Size Image databases are comparatively bigger than traditional text databases Indexing techniques further increase the database size Performance As the size of the database increases, performance decreases Complexity Managing image database and retrieving images is complex
16 Future image databases.. Photobook - MIT It works by comparing features associated with images VisualSEEK joint content-based/spatial querying automated region extraction direct indexing of color features Blob World - Berkley sophisticated color region searching IBM's Query by Image Content (QBIC) color percentages, color layout, & textures QBIC in DB2 in HermatigeMuseum etsy by color
17 Technology: IBM DB2 Image Extender DB2 Universal Database (UDB) is a product of IBM It offers the DB2 Image Extender as part of the DB2 UDB as Audio, Image and Video Extenders package The DB2 Image Extender defines the distinct data type DB2IMAGE The following SQL-insert statement shows how an image is stored into an image database INSERT INTO example (image) VALUES( DB2IMAGE (CURRENT SERVER, pisa.jpg, /* source_file */ JPG, /* source_format */ 1, /* 1=BLOB, 2=file pointer */ my Image File /* comment */)) In this example, the content of the image comes from a server file and stored as a BLOB in the database.
18 DB2 Image Extender... contd Supports a variety of image formats like GIF, JPEG, BMP, and TIFF Creates and stores a thumbnail of the image. It is also used to retrieve a thumbnail or a full-size image Searches the images based on data that you maintain, such as a name, number, or description It can also search by data that the DB2 Image Extender maintains, such as the format of the image or its distribution of colors, etc.
19 Professional Examples IrfanView Graphic viewer for professionals as well beginners Thumbnail/preview option Show EXIF/IPTC/Comment text in Slideshow /Fullscreen etc. IPTC editing possible Metadata can be edited after installing plugin IPTC.dll It is free!! Disadvantage: Does not allow editing EXIF
20 Professional Examples IrfanView contd...
21 Professional Examples PixFiler It is a picture organizer Fast and robust Easy to categorize and annotate Information can be retrieved from EXIF and ITPC metadata Disadvantage: It is not free :(
22 Professional Examples ImageMagick Allows to read, write and manipulate images Supports 89 image formats including TIFF, JPEG,PNG,GIF, etc. Works well with Windows, Unix and Mac Provides interface for programming languages like C/C++, Java, Perl, PHP and other Biggest advantage is that interface can connect ImageMagick with DB2 Image Extender ImageMagick is free!
23 Consumer Examples Facebook Flickr ImageShack Most consumer examples are websites because of their remote accessibility, ease-of-use, and low to zero cost. These examples are some of the most popular ones used today. ImageShack - most images on the web today Consumer examples typically lack advanced search options such as querying by image content. Must search by image metadata - tags, captions, filenames, etc
26 Industrial example Satellite Imagery in Earth Science Applications(Remote Sensing) Google Earth Medical Image Database: MedPix Oil industry:iesx Digital image catalog: ARTstor
27 Google Earth Google Earth: image store, rotate, multiresolution display, 3-D models, etc.
28 MedPix Store images of radiography, computed tomography (CT), magnetic resonance imaging (MRI)...
29 IESX Used at oil industry for oil exploration: seismic survey and interpretation, strata imagery, etc.
30 ARTstor Digital image library for educational purpose. Support for metadata integration, multiresolution storage, zoom, rapid retrieval, and integration with traditional database.
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