Alexander Gelbukh www.Gelbukh.com Special Topics in Computer Science Advanced Topics in Information Retrieval Lecture 5 (book chapter 11): Multimedia IR: Models and Languages Alexander Gelbukh www.Gelbukh.com

Previous Chapter: Conclusions Inverted files seem to be the best option Other structures are good for specific cases Genetic databases Sequential searching is an integral part of many indexing-based search techniques Many methods to improve sequential searching Compression can be integrated with search

Previous Chapter: Research topics Perhaps, new details in integration of compression and search “Linguistic” indexing: allowing linguistic variations Search in plural or only singular Search with or without synonyms

Motivation Applications: Example: office, CAD, medical, Internet Artists sings a melody and sees all the songs with similar melody

What’s different Different from text IR: Aspects: Structure of data is more complex. Efficiency is an issue Using of metadata Characteristics of multimedia data Operations to be performed Aspects: Data modeling: Extract and maintain the features of objects Data retrieval: based not only on description but on content

Retrieval process Query specification fuzzy predicates: similar to content predicates: images containing an apple data type predicates: video, ... Query processing and optimization Parsed, compiled, optimized for order of execution Problem: many data types, different processing for each Answer Relevance: similarity to query Iteration Bad quality, so need to refine

Modeling

Data modeling To model is to simplify, in order to make manageable. “We will represent an image as...” From the user’s point of view From the system’s point of view (technically) A problem: very large storage size. Modeling needed Objects are represented as feature vectors Images / Video: shape. House, car, ... Sound: style. Music: Merry, sad, ... Features are defined directly or by comparison Degree of certainty is stored

Multimedia support in commercial DBMSs (1999) Variable length data. Non-standard Different and usually very limited sets of operations SQL3: provides user-extensible data types Object-oriented Implemented partially in many systems Example: data blades of Informix Content-based functions on text and images E.g.: date = 1997 AND contains (car)

Spatial data types Informix: 2D, 3D data blades Boxes, vectors, ... Operations: intersect, contains, center, ... Text: containWords, .... Supports query images by content

Example: MULTOS Multimedia document server Documents are described by: logical structure: title, into, chapter, ... layout structure: pages, frames, ... conceptual structure: allows content-based queries Docs similar in conceptual structures are grouped into conceptual types Example: Generic_Letter

Example of conceptual structure...

...continued

Image data in MULTOS Analysis Result of analysis: low level: detect objects and positions high level: image interpretation Result of analysis: description of objects found and their classes certainty values Indices are used for fast access to this info Object index. Includes pointers to objects and certainty values Cluster index, with fuzzy clusters of similar images

Internet How Google does it? No image processing. Textual context! File names, nearby words Distance from image to words “give me images with flower in the file name or near the image”

Languages

Query languages As a query, either a description of the object or an example object is submitted “show me images similar to this one” in what respects similar?! Exact match is inadequate. Additional means are needed Content is not a single feature

What defines query language Interface. How to enter the query Types of conditions to specify Handling of uncertainty, proximity, weights

Interface Browsing and navigation Search: description or query by example Query by example: specify what features are important. Give me all houses with similar shape but different colors Libraries of examples can be provided

Conditions... Attribute predicates Structural predicates structured content – the predefined types extracted beforehand Exact match. E.g.: size, type (video, audio, ...) Structural predicates structure: title, sections, ... metadata are used. Find objects containing an image and a video clip Semantic predicates unrestricted content. Find all red houses: red = ?, house = ? Fuzzy

... conditions Predicates Spatial: contain, intersect, is contained in, is adjacent to ... Temporal: Find audio where first politics and then economy is discussed Spatial and temporal predicates can be combined: Find clips where the logo disappears and then a graph appears at the same place A predicate can be applied to a part of document As path expressions in OO databases

Uncertainty, proximity, weights Similarity function The user can assign importance weights to individual predicates in a complex query This gives ranking, as in text IR The same models can be used, e.g., probabilistic model

Examples of query languages: SQL3 Functions and stored procedures: user-defined data manipulation Active database support: database reacts on the events, not only commands. This enforces integrity constraints Good news: rather standard Bad news: no ranking supported! Effort to integrate SQL3 with IR techniques. SQL MM Full Text and other similar languages

... examples: MULTOS One of design goals: easy navigation Paths are supported Identification of components by type, not by position All images in the document, not the image in 3rd chapter Types of predicates: on data attributes, on textual components, on images (image type, objects contained, ...) Example:

MULTOS example

Another example of MULTOS

Research topics How similarity function can be defined? What features of images (video, sound) there are? How to better specify the importance of individual features? (Give me similar houses: similar = size? color? strructure? Architectural style?) How to determine the objects in an image? Integration with DBMSs and SQL for fast access and rich semantics Integration with XML Ranking: by similarity, taking into account history, profile

Conclusions Basically, images are handled as text described them Namely, feature vectors (or feature hierarchies) Context can be used when available to determine features Also, queries by example are common From the point of view of DBMS, integration with IR and multimedia-specific techniques is needed Object-oriented technology is adequate

Thank you! Till ??, 6 pm