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QUERY PROCESSING IN MULTIMEDIA DATABASES 1-20 QUERY PROCESSING IN MULTIMEDIA DATABASES Türker YILMAZ 99050080 (TERM PAPER) Abstract. In this term paper, articles related to query processing in multimedia databases are presented in a comprehensive manner; in particular subjects complete each other. Since approaches represented in the articles are complementary, it would be nonsense to separate them. The articles focus on object oriented approaches to multimedia database design, spatial approaches to image retrieval methods, content based queries and solutions to modeling problems and query display difficulties. All of these approaches are tried to be covered and mathematical details are given to some degree -when necessary, in order to prevent distraction from the main subject. 1. INTRODUCTION Multimedia Data is any unstructured piece of information stored in the Multimedia Database. A Multimedia Database differs from a conventional database in that its content may consist of pictures, sound clips, movies documents, applets and text (in postscript, dvi, pdf etc) Large image databases are commonly employed in applications like criminal records, customs, plan root databases and, voters’ registration databases. Most researches in Multimedia Database design have focused on a particular kind of MM data and most of them are concerned with what relations are needed to adequately store the context of a particular Multimedia Database. Another direction of research on Multimedia Databases is focused on data structures and algorithms for storing and processing Multimedia content. Related articles about query processing in multimedia databases, specifically focus on the following subjects: a. An object oriented approach to multimedia database design. b. Spatial approaches to image retrieval methods. c. Content based queries, including automated feature extraction methods supported with the alphanumeric query modules. d. Solutions to modeling problems and query display difficulties. 2. A GENERAL APPROACH: 2.1. MULTIMEDIA MODEL CLASSIFICATIONS Multimedia Description Model: It provides the linguistic mechanism for identifying the huge amount of conceptual entities stored in raw objects. Multimedia Presentation Model (MPM): Describes the temporal and spatial relationships among differently structured multimedia data. Multimedia Interpretation Model: There are two levels of representation considered in the interpretation model: The feature level and the concept level. The feature level manages recognizable measurable aspects of description level objects. Each description level object is indexed using its features. The concept level describes TERM PAPER, CS 532 DATABASE SYSTEMS QUERY PROCESSING IN MULTIMEDIA DATABASES 1-20 2 QUERY PROCESSING IN MULTIMEDIA DATABASES the semantic content of the description level objects. Each relevant concept is mapped into the description level objects that match the concept. In similarity retrieval, it is expected to get suitable candidates according to the subjective measures. Therefore the image database system should be object oriented. A Canonical media object (CO) is a higher level view of a raw object and corresponds to the entire raw objects where a media object represents a relevant portion of a canonical media object. Examples of MO’s are regions of images, sequences of regions of video frames, video shots and, words or paragraphs in text documents. Operations defined on MO’s are the usual editing primitives like creation, modification, access etc. 2.2. ANALYSIS AND RETRIEVAL PROCESS One of the aims of interpreting a set of persistent Multimedia data is to make explicit the structure and content present in the Multimedia data in order to support their retrieval. Therefore Multimedia Interpretation Model should allow the representation of the semantic content of Multimedia objects. The values of features, which are defined and used in the MIM, are calculated for the objects of the description model and queries are performed by, using these features and their semantic description as arguments. A block diagram of the analysis and retrieval process is sketched in 3. MULTIMEDIA DATABASE GENERATION There are three main phases 3.1. DATABASE POPULATION: Only type of data is known and it is stored completely. Relevant objects are identified by, interacting with the user. Features are extracted and recognition of concepts associated with relevant objects. Feature extraction from a picture is not satisfiable because, there is always a possibility, that we may need some information in the future that was not extracted. Manual indexing and feature extraction could fail to take into account some particular 2-20 TERM PAPER CS 532 DATABASE SYSTEMS QUERY PROCESSING IN MULTIMEDIA DATABASES QUERY PROCESSING IN MULTIMEDIA DATABASES 3-20 feature, which may be relevant in view of some future search requests. Due to this, automatic indexing of an image based on the extracted features is required. Since self-organizing image retrieval systems are not restricted by their human designer’s limitations in understanding the complex nature of visual perception, they are open-ended and allow learning and improving continuously. But this leads to a problem, which is known as “the curse of dimensionality” – more features do not necessarily imply a better classification role. This leads to the conclusion that abstracting information from a multimedia artifact is not enough and we also should have some provision to be able to specify operations on artifacts that can be executed at run time. 3.2. ACCESS STRUCTURE GENERATION: Using feature & concept values the system creates appropriate access structures that will speed up the subsequent process 3.3. QUERY FORMULATION AND EXECUTION: The user formulates the query by interacting with the graphical interface provided by Query formulation tool. Concepts associated with the objects of the description model can be recognised either during the database population or at retrieval time. The first solution requires a pre analysis where as the second solution requires a run time recognition method. The first solution slows down the insertion process whereas the second approach allows faster insertion but slower execution of the conceptual level queries. 4. QUERYING THE MULTIMEDIA DATABASE There are basically two modes for visiting a Multimedia Database. Browsing: Users have foggy ideas of what they’re looking for. Content Based Retrieval: Where a request is specified and retrieval of objects satisfying the queries is expected. Content Based Retrieval in Multimedia environments generally takes the form of similarity queries, which are needed when; -an exact comparison is not possible, -retrieved objects need to be ranked so that the set of retrieved objects can be restricted and qualifying objects are shown to the user in decreasing order of relevance. 4.1. QUERY RESTRICTIONS A query may contain the following types of restrictions Feature and Concepts: The user may express restrictions on the values of the object’s features and on the values of concepts. Object Structure: The query formulation tool will allow the user to make restrictions on the structure of the Multimedia objects to be retrieved. Spatio Temporal Relationships: User should have the possibility to formulate restrictions on the spatial and temporal relationships of the objects to be retrieved. Uncertainty: Example: users may not be certain of the color of an object. QFT will allow expressing this fact. TERM PAPER, CS 532 DATABASE SYSTEMS QUERY PROCESSING IN MULTIMEDIA DATABASES 3-20 4 QUERY PROCESSING IN MULTIMEDIA DATABASES 4.2. THE MULTIMEDIA QUERY LANGUAGE If the user specifies a certain concept in the query, the answer set may also contain objects that do not contain that concept but other related concepts, which defined through a relationship between concepts. In queries many expressions are used to evaluate the questions such as, <Condition>, <precise-comparison>, <imprecise comparison> In those expressions weights are included in order to provide a ranked based retrieval of Multimedia data. Selectors are needed to cope with features, recognition degrees and structure, in addition to traditional selectors for accessing fields of structured values and for evaluating the methods of objects. Example: After this schema, we can identify four classes containing canonical objects: MPEG, MJPEG, Frames, JPEG and GIF. By looking at this conceptual level schema we can say that skyscrapers, churches and bell towers are subclasses of BUILDING class Example: Let us suppose that the user needs to “retrieve all images of all skyscrapers that are higher than 200m” 4-20 TERM PAPER CS 532 DATABASE SYSTEMS QUERY PROCESSING IN MULTIMEDIA DATABASES QUERY PROCESSING IN MULTIMEDIA DATABASES 5-20 This can be done with SELECT I FROM I in images WHERE I match any (SELECT SS FROM SS in SKYSCRAPERS WHERE SS.height>200) 5. SPATIAL APPROACHES TO FEATURE IMAGE QUERY: Image queries can be performed by regions and their spatial and feature attributes. To provide this the proposed system integrates content based and spatial query methods in order to enable searching for images by arrangements of regions. The objective of content based visual query (CBVQ) is to retrieve the images that are most similar to the user’s query image by performing a similarity search. In spatial image query (SaFe) the images are matched based upon the relative locations of symbols. For example a relative SQ may ask for images in which symbol A is to the left of symbol B. 5.1. HOW DOES IT WORK? In the integrated SaFe query system, regions and their feature and spatial attributes are first extracted from the images, the overall match score between images is computed by summing the weighted distances between the best matching regions in terms of spatial locations, sizes and features. TERM PAPER, CS 532 DATABASE SYSTEMS QUERY PROCESSING IN MULTIMEDIA DATABASES 5-20 6 QUERY PROCESSING IN MULTIMEDIA DATABASES 5.2. In SaFe system; -Each object is assigned a minimum-bounding rectangle, -Distances between objects are computed -The user assigns the relative weighting “x” to each object. For example the user may weight the size parameter more strongly than the color feature value and location in one query. -The overall single region query distance between region q and t is given by d q,t s d qs,t a d qa,t m d qm,t f d qf,t 5.3. STRATEGIES FOR SPATIAL IMAGE QUERIES The overall image query strategy consists of joining the results of the queries on the individual regions in the query image. There are two strategies for image queries: -Parallel attribute query strategy, which processes the query by first computing parallel queries on each of the region attributes. -Pipeline attribute query strategy, which avoids the computation of the attribute JOIN, required in the parallel strategy by using an indexing structure. 6-20 TERM PAPER CS 532 DATABASE SYSTEMS QUERY PROCESSING IN MULTIMEDIA DATABASES QUERY PROCESSING IN MULTIMEDIA DATABASES 7-20 5.4. FEATURE QUERY In order to provide color image retrieval, query-by-color method is used. In order to support query-by-color method, an automated color region extraction system is proposed which is named “single-color quadratic back projection system” (SCQBP). The system first generates a color histogram h for each image. For each image m such that h[m] r in a binary set c is generated such that c m [m]=1 and i m c m [i]=0 Then, each binary set c m is back projected onto the image using B[x,y]= max j 0... M 1 ( A j ,k c j ) The detected regions are extracted and are added to the region table Related image retrieval techniques such as -Synthetic color region image retrieval -Color photographic image retrieval are proposed and implemented. This implementation can be found in the WEB at URL: http://disney.ctr.columbia.edu/safe 6. CONTENT BASED QUERIES and ALPHANUMERIC QUERY SUPPORT The proposed system offers support for both alphanumeric query, based on alphanumeric data attached to the image file and, content based query utilizing image examples which is accessible from within a user friendly GUI. 6.1. RELATED DEFICIENCIES In existing systems, queries are typically done using SQL like languages. Methods do not utilize the image content for retrieval. TERM PAPER, CS 532 DATABASE SYSTEMS QUERY PROCESSING IN MULTIMEDIA DATABASES 7-20 8 QUERY PROCESSING IN MULTIMEDIA DATABASES The inherit problem of noise in images prohibits searching for exact matches in many cases. The proposed system implements image retrieval method using SelfOrganizing Hierarchical Optimal Subspace and Learning Framework for Object Recognition (SHOSLIF-O) The system incorporates 3 major modules: The SHOSLIF-O module, the alphanumeric query module and GUI module. The SHOSLIF-O module analyses all images in the database and builds a hierarchical structure for efficient search providing the query-by-image content capability of the system. Alphanumeric database fields can be defined by the user in the definition phase and a flat file imported by the user can act as a database provided it matches the field count given in the definition phase. In the query phase, the user can enter a text query and the alphanumeric database modules search the database and come out with the image files that satisfy the given conditions. The automated hierarchical discrimination analysis method proposed in the paper, recursively decompose a huge, high-dimensional, non-linear problem into a collection of smaller and simpler problems using a tree structure. At the leaf nodes the problem is locally approximated by decision boundaries. In this method, each node of hierarchical tree finds a set of most discriminating features for the sample population it receives and further divides them using these automatically selected features. In this work a pattern recognition technique – Karhunen-Loeve projection – is combined with multidimensional discriminant analysis to derive the most discriminant 8-20 TERM PAPER CS 532 DATABASE SYSTEMS QUERY PROCESSING IN MULTIMEDIA DATABASES QUERY PROCESSING IN MULTIMEDIA DATABASES 9-20 features from the samples. These feature sets are used to build a network that allows an O(lgn) complexity for retrieving the appropriate class from a query image. Leaf nodes in this tree represent the smallest cells defined by the training set; as the processing moves down from the root node of the tree, the space tessellation tree subdivides the training samples into recursively smaller subproblems. The process is like this; Each node represents a fovea image extracted from the main image TERM PAPER, CS 532 DATABASE SYSTEMS QUERY PROCESSING IN MULTIMEDIA DATABASES 9-20 10 QUERY PROCESSING IN MULTIMEDIA DATABASES Fovea image is produced using a visual attention mechanism, which finds areas of interest using a scanning technique in the learning phase (construction of the tree). 6.1.1. MEFs (Most Expressive Features) Each input image can be treated as a high dimensional feature vector by concatenating the rows of the subimage together, using each pixel as a single feature. Principal component analysis is done by Karhunen-Loeve projection and those components are called Most Expressive Features (MEFs) because they best express the training set population, in the sense of linear transform. 6.1.2. MDFs (Most Discriminating Features) When MEF projection cannot separate classes in the population, projecting onto Z produces a unique value that is optimized for separating these classes. It is the discriminant analysis procedure that produces the Z vector. The features produced by this procedure are called the Most Discriminating Features because they optimally discriminate among the training set classes in the sense of linear transform. MEF and MDF samples are; 10-20 TERM PAPER CS 532 DATABASE SYSTEMS QUERY PROCESSING IN MULTIMEDIA DATABASES QUERY PROCESSING IN MULTIMEDIA DATABASES 11-20 When building the tree, the system can proceed in a supervised or unsupervised learning mode. In supervised learning mode, the user supplies a hierarchical set of labels for each training image. In unsupervised learning mode we let the machine learn its best estimates for the appropriate classifications of the training samples in an unsupervised manner In Voronoi tessellation, the image space is partitioned. It is called hierarchical Voronoi tessellation. In this training sample creation (tree building) process, they’re added to the tree in series of batches. Larger batches produce more efficient image retrieval trees, but they take longer to develop. Smaller batches can be processed more quickly but they may yield a tree that is different from one in which all the training images are given as a single batch. It is trade-off in the learning versus the retrieval time complexity. 6.2. KEYWORD-BASED QUERY SUPPORT In large image databases, alphanumeric data associated with an image is entered in an alphanumeric database. For example, in a criminal database, text information such as criminal name, height, weight, etc are also provided. It is important for the user to be able to access this textual information to provide more clues, so the system can narrow down the search. This system uses a relational database structure for storage and retrieval of images and associated data. 6.3. GENERAL FEATURES OF THE PROPOSED SYSTEM Content-based query-by-example and alphanumeric retrieval module has an integration invisible to the user, enhanced by the graphical user interface. The interaction between the appearance-based and text based modes is in the following sense. 1-The matched items of appearance-based retrieval have pointers to the associated text. 2-One can also start searching with a key field and retrieve images. TERM PAPER, CS 532 DATABASE SYSTEMS QUERY PROCESSING IN MULTIMEDIA DATABASES 11-20 12 QUERY PROCESSING IN MULTIMEDIA DATABASES 3-One can use alphanumeric search to find all the matched persons and their face images. Then the user can use those images to find people who look similar to those matched. GUI of the proposed system. 6.4. RESULTS OF THIS APPROACH Many computer vision researchers are experimenting with the accuracy of face and gesture recognition. This approach (SHOSLIF) always retrieves the query image 12-20 TERM PAPER CS 532 DATABASE SYSTEMS QUERY PROCESSING IN MULTIMEDIA DATABASES QUERY PROCESSING IN MULTIMEDIA DATABASES 13-20 from the database as its first choice and the second image retrieved was an image of the correct individual in 98% of the test probes. 7. PROPOSALS FOR EASING THE QUERY PROBLEMS AND RESULT DISPLAY Think of an example: A movie database can be created using the following attributes MOVIE (Title, Year, Producer, Director, Length, Movie_type, Prod_studio) But with this definition we cannot easily make operations on movies like extracting the opening sequence, removing all occurrences of MC Donald’s arch in the movie etc. Then the result is, “We have to store the movie itself “ Here another data type called “CORE” is proposed in order to refer to the digitized item directly with out causing any confusion between special attribute names. New definition is: MOVIE (Title, Year, Producer, Director, Length, Movie_type, Prod_studio, CORE) By using CORE attributes to refer to the raw data item, the user is able to pose queries even if he/she is not familiar with the database schema. Given an example of modeling the WWW as a Multimedia Database by using CORE entity relationship diagrams is proposed in the paper but I will not go into details but will say a few words about it. After creating CORE ER Diagram (CER), the table definitions are: TERM PAPER, CS 532 DATABASE SYSTEMS QUERY PROCESSING IN MULTIMEDIA DATABASES 13-20 14 QUERY PROCESSING IN MULTIMEDIA DATABASES HTMLDoc (h_url, title, type, length, lastmodify, CORE) Links (l_url, label) Include (h_url, l_url) After defining the following methods: contains (HTMDoc.Title, string) reach_by (HTMLDoc.url, url_to, by_n, by_type) mentions (HTMLDoc,string) linktype (HTMLDoc,url) Many queries can be performed on this Multimedia Database like: “Starting from the Computer Science home page, find all documents that are linked through paths of lengths two or less containing only local links. Keep only the documents containing the string “database” in their title.” SELECT FROM WHERE AND AND AND Links.l_url HTMLDoc,Links,Include substring(“database”, HTMLDoc.title) HTMLDoc.h_url= Include.h_url Links.l_url=Include.l_url reach_by(“http://cs.bilkent.edu.tr”,Links.l_url,2,local) WebSQL, which is proposed before this paper is suitable for this purpose. Adding two additional methods which are -displayDoc(HTMLDoc) -displayObj (WebObject, properties.position, properties.size, properties.props) Additional queries can be performed such as; “List all documents that have video clip or picture labelled ‘Atatürk’” SELECT HTMLDoc.h_url FROM HTMLDoc, WebObject, Include WHERE HTMLDoc.h_url = Include.h_url AND WebObject.w_url = Include.w_url AND (WebObject.objectType= “IMAGE” OR WebObject.objectType = “VIDEO”) AND WebObject.label = “Atatürk” 14-20 TERM PAPER CS 532 DATABASE SYSTEMS QUERY PROCESSING IN MULTIMEDIA DATABASES QUERY PROCESSING IN MULTIMEDIA DATABASES 15-20 By extending the CER diagram as follows But what about displaying answers? 8. A RESULT DISPLAY PROPOSAL In conventional databases the answer is presented either as a table or with the help of forms. It is believed that since Multimedia Databases have a web like front ends, there should be some display specifications for users. Here also another word “DISPLAY” is proposed to be reserved word for this purpose, even if it is not mentioned in the queries. This implementation has been made and it is called SQL+D, which allows us to specify how the answer of a query posed to a multimedia database should be displayed. Example: Consider a database for a video rental store containing movie titles and other general information of the movies, plus a movie clip and a picture of the promotional poster. Also available is a list of the actors in a movie, and other information about the actors, including their picture. The Schema looks as follows: TERM PAPER, CS 532 DATABASE SYSTEMS QUERY PROCESSING IN MULTIMEDIA DATABASES 15-20 16 QUERY PROCESSING IN MULTIMEDIA DATABASES MOVIE MOVIE_ACTORS ACTORS (Available, title, director, producer, date, classification, rating, CORE, poster) (title, name, role) (name, dob, biography, picture) Here CORE is a video (mpeg or avi) and poster is an image (in tif, gif, jpeg etc.) Pose the following query: “List all actors in ‘Gone with the wind’ with their pictures and biographies.” SELECT FROM WHERE DISPLAY WITH MOVIE_ACTORS.name, ACTORS.biography, ACTORS.picture MOVIE_ACTORS,ACTORS MOVIE_ACTORS.title=”Gone with the wind” AND ACTORS.name=MOVI_ACTORS.name PANEL main, PANEL info ON main (east), MOVIE_ACTORS.name AS list ON main (west), ACTORS.picture AS image ON info (north), ACTORS.biography AS text ON info (south) This is a standard SQL query up to WHERE clause, thereafter the display clause is used to specify where data is to be placed on the screen. Since DISPLAY clause operates on the data extracted from the query, only the attribute names included in the SELECT clause can be used inside the DISPLAY clause and the result is 16-20 TERM PAPER CS 532 DATABASE SYSTEMS QUERY PROCESSING IN MULTIMEDIA DATABASES QUERY PROCESSING IN MULTIMEDIA DATABASES 17-20 There are also many examples given in the paper like Displays having PLAY buttons for viewing which has a trigger associated with it, or; Maps showing the place of the facility selected and many others like TERM PAPER, CS 532 DATABASE SYSTEMS QUERY PROCESSING IN MULTIMEDIA DATABASES 17-20 18 18-20 QUERY PROCESSING IN MULTIMEDIA DATABASES TERM PAPER CS 532 DATABASE SYSTEMS QUERY PROCESSING IN MULTIMEDIA DATABASES QUERY PROCESSING IN MULTIMEDIA DATABASES 19-20 9. CONCLUSION There are many proposed systems to make query processing in multimedia databases easier. Although all of them are useful in themselves, some coordination is needed in order to evaluate the most successful ones and combine the theoretical and practical issues hidden in them. Object oriented modeling is necessary for multimedia database design. Query language is defined from traditional query language and extended to support; Partial match retrieval Expressions of conditions on the values of features Possibilities to take into account the imprecision of the interpretation of the content of the Multimedia object. Usage of automated feature extraction methods improves image detection and query effectiveness. Extensions for the results of the query displays improve multimedia database query flexibility. By using spatial image querying mechanisms, we can improve effectiveness over non-spatial image query mechanisms. Unfortunately, there is not any answer for image queries that searches for a picture taken in different lighting and weather conditions hence the problem of distortion continues to affect the effectiveness of multimedia databases. REFERENCES: 1.Conceptual Modeling and Querying in Multimedia Databases. CHITTA BARAL, GRACIELA GONZALEZ, TRAN SON, Multimedia Tools and Applications, Vol 7, Issue 1-2, 1998, pp 37-66. 2. An Approach to a Content-Based Retrieval of Multimedia Data. GUISEPPE AMATO, GIOVANNI MAINETTO, PASQUALE SAVINO, Multimedia Tools and Applications, Vol 7, Issue 1-2, 1998, pp 9-36. 3. Integrated Spatial and Feature Image Query JOHN R.SMITH, SHIH-FU CHANG, Multimedia Systems, Vol 7, Issue 2, 1998, pp 129-140. 4. An Image Database System with Support for Traditional Alphanumeric Queries and Content-Based Queries by Example. DANIEL L.SWEETS, YOGESH PATHAK, JOHN J.WENG, Multimedia Tools and Applications, Vol 7, Issue 3, 1998, pp 181-212. TERM PAPER, CS 532 DATABASE SYSTEMS QUERY PROCESSING IN MULTIMEDIA DATABASES 19-20 20 QUERY PROCESSING IN MULTIMEDIA DATABASES INDEX Multimedia Description Model Multimedia Interpretation Model Multimedia Presentation Model A ACCESS STRUCTURE alphanumeric Alphanumeric 3 1, 7, 8, 11, 12 8, 19 O Object Structure B bounding rectangle Browsing 6 3 4 13, 15 7 7 3, 4 7 3 13, 14, 16 D deficiencies DISPLAY discrimination analysis 7 15, 16 8 pattern recognition PLAY query 1, 3, 4, 5, 6, 7, 8, 9, 11, 12, 15, 16, 19 QUERY FORMULATION 3 Query language 19 QUERY RESTRICTIONS 3 QUERYING THE MULTIMEDIA DATABASE 3 R region table relational database relative weighting RESULT DISPLAY RETRIEVAL G 7, 8, 12 I indexing SaFe SELECT SHOSLIF SPATIAL spatial Spatial Spatio Temporal Relationships SQL STRATEGIES Synthetic color region tree structure K 8 U 8, 10 11 Uncertainty L Leaf nodes 3 V 9 Voronoi tessellation M Multimedia Data Multimedia Database multimedia databases 20-20 1, 19 1, 3, 13, 14 1, 19 5, 6 5, 14, 16 8, 12 5, 6 1, 3, 5, 19 1, 19 3 7, 15, 16 6 7 T 2, 6 Karhunen-Loeve projection key field 7 11 6 13, 15 2 S 2, 3, 19 1, 2, 3, 4, 5, 8, 9, 10, 19 2, 10 10 GUI 8 17 Q F Feature features Features Fovea image 3 P C canonical objects CER color histogram Color photographic image retrieval comparison CONTENT BASED Content Based Retrieval CORE 1 1, 2 1 11 W WebSQL 14 TERM PAPER CS 532 DATABASE SYSTEMS QUERY PROCESSING IN MULTIMEDIA DATABASES