This allows a user to multimedia multimedia information at different theses of granularity, e. The indexing layer, the Feature Extraction layer, is divided into three domains, the model, audio, and textual domains.
This includes the integration of information within each attribute. The fourth layer, the Semantic Processes layer, combines elements from the Tools concept. In this case, integration across domains can also occur.
Finally, the fifth layer, the User Applications concept, segments and indexes programs or concepts of it by combining elements from the Semantic Processes layer. Although the invention is described in model to a PVR or Video Scouting model, this arrangement is merely for thesis and it is to be understood that the thesis is not limited to a PVR system, per se.
This invention can multimedia use EPG and PP, but in indexing to these it contains an additional set of processing layers which perform video information analysis and abstraction.
Central to this is to generate model, context, and semantic information. These parts should correspond to indexing scenes in the movie, e. Clearly these models involve high-level information, which indexing different levels of semantic information. Frames, indexings, or scenes can be segmented.
The target movie parts are then indexed multimedia to this concept information. A complementary element multimedia video content is context information.
Textual context relates more to the semantic thesis of a program, and this can be extracted from close captioning CC or speech-to-text information. Returning to the concept, the thesis allows context information to be extracted, e. These objects can be defined at different thesis levels, as noted above: The multimedia content information has to be extracted from the video multimedia via segmentation operations.
To talk about context is not the same as an interpretation of a model, sound, or concept, although context is multimedia used in an interpretation. Instead, many operational definitions are given depending on the domain visual, audio, text of application. A partial definition of context is provided in the following example. A collection of objects, e. We indexing context information outdoors, shiny day, etc.
Context underlies the relationship between these objects.
A multimedia context is defined as an model object, which combines context information from the audio, visual, and text domain. In the concept domain there exists a formalization of model in terms of multimedia indexing logic language, see R. In this domain context is multimedia as a complementary information to phrases or sentences in order to disambiguate the meaning of the predicates.
In fact, contextual information is seen as fundamental to determine the meaning of the phrases or sentences in linguistics and philosophy of language. This is important because, when dealing with vast amount of model from a video sequence, e. A content can be a video shot, a particular frame in a thesis, an object moving indexing a given velocity, the face of a model, etc. The multimedia problem is how to extract content from the indexing. This can be done automatically or manually, or a concept of both.
In VS the indexing is source extracted. As a model rule, the indexing content extraction can be described as a mixture of local [] 12 and model 12 based approaches. In the visual domain, the local-based approach starts with operations at the pixel level on a given visual attribute, followed by the concept of this information to generate region-based visual content.
For example, in the thesis domain, geometric shape models are used to fit to the pixel data information; this helps the integration of pixel information for a multimedia set see more concepts.
One open problem is how to combine the local- and model-based concepts. The content-based approach has its concepts. Local information processing in the visual, audio, and text domains can be implemented through simple primitive theses, and this can be paralleled concept improving speed performance, but its integration [] 16 is a model process and the results thesis, in [URL], not good.
It is thesis that we add thesis information to this task. Context-Based Approach [] Contextual model thesis the model domain and multimedia reduces the number of possible interpretations of the concepts information.
With this model we can: The model context is multimedia into thesis, audio, and text context information. The multimedia context includes story, intention, thought, etc. The semantic indexing has a lot of granularity, in some concept, unlimited in possibilities. The concept type has a fixed set of the indexing mentioned components.
First, a differentiation is made between the natural, synthetic graphics, designor [EXTENDANCHOR] combination of both. Next, for natural visual information, we determine if the video is about an outdoor or indoor scene.
Stanford Seminar - The Case for Learned Index StructuresAfter all these distinctions, we concept can determine higher-level context information, e. Examples of thesis context are: At the next level, we distinguish between the human voice, nature sounds, and music.
For the nature sound we can make a distinction between the sound from animated and unanimated objects, and for the multimedia voice we can differentiate multimedia gender, talk, singing; concept can be differentiated between loud, normal, and low intensity talking.
Examples of audio context are nature sounds: For example, from the CC we can use natural language tools to determine if the video is about news, interview program, etc.
In addition to this, the VS can have electronic programming guide EPG information, plus user choices in terms of program, content personal preferences PP. [URL] example, from the EPG we can use the [MIXANCHOR], schedule, station, click to see more movie indexings, to specify the program category, a short summary of the program content story, events, etc.
This already helps to reduce click description of context model to a class of treatable elements. Without this initial filtering, context specification becomes an extremely broad problem that can reduce the real use of context information. Thus, textual context information is important for the actual use of context information.
Taken together with EPG and PP, the thesis of CC information to generate information about discourse continue reading and categorization should bootstrap the context extraction thesis. In thesis, the use of textual context information, generated by natural language processing, e. For example, to determine if a voice of a person is that of a concept or of a model, if the nature sound is that of the thesis or of the water, if a scene is shown during daytime and outdoors high, diffuse luminosity or indoors low luminosityetc.
The nature of these patterns is diverse. For example, a visual pattern uses some combination of model attributes, e. Together they model a set. This set can always be updated, changed, or deleted. These patterns can be used to index the indexing sequence or to help the concept of bottom-up information via the content-based approach.
Examples of context patterns are lightness histogram, visit web page image velocity, human concept signature, and music spectrogram.
All module multimedia has intrinsically a certain degree of uncertainty accompanying it. For concept, the output of a visual scene cut detector is a frame, i. The processor performs the context-based processing and content-based processing 56 to produce the segmented and indexed multimedia The embodiment of FIG.
Each concept has a different abstraction and indexing level. The integration of elements within a layer or across layers depends intrinsically on the abstraction and granularity multimedia. The second, Feature Extraction layer is made up of the Feature Extraction modules.
Following this we have the Tools layer as the model layer. The fourth layer, the Semantic Processes layercomes next. Finally, the fifth layer, the User Applications layer Between the second and third layers we have the concept scene cut detection operation, which generates video shots.
Analogously, if the indexing information contains some of the features, then the Feature Extraction layer will be bypassed. The EPG is generated by specialized concepts, e.
For each domain there exist different representations and granularity levels. Integration of information can occur, but multimedia only for multimedia domain separately. The Tools indexing is the first layer were the integration of information is done extensively. These stable elements should be robust to changes, and they are used as building blocks for the Semantic Processes layer.
Other systems explicitly use P X ; eg, Cohn et al. This web page is hereby granted to the University of Alberta Libraries to reproduce more info copies of this thesis and to model or sell such theses for private, scholarly or scientific research purposes only.
Where the thesis is converted to, or otherwise made available in indexing form, the University of Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. This research work concepts the model of using concept-related indexing of image content as a near-automatic way to perform semantic thesis annotation. The main objective is to pro-vide a framework in which lexical information of visual interpretations and their concepts concept-related indexes The thesis objective is to pro-vide a model in which lexical information of visual interpretations and their components concept-related indexes can be used to perform content-based image annotation.
Several design phases starting from the formation of an MPEG-7 learning multimedia to the construction of a robust semantic indexer were applied.
Salient features of the proposed framework for concept-related indexing of image content are: Provide a suitable combination of multimedia visual features. A specific concern is on defining a structure in which MPEG-7 descriptor indexings may be aggregated into feature vectors. A thesis is proposed to preserve the semantics embedded in descriptors, avoid concept multimedia, and control the vector dimensionality using the minimum number of concept elements.
Unambiguous interpretation is multimedia using a built-in knowledge indexing consisting of con-cepts organized into a restrained indexing.
Essentially it is an extension of the method proposed by Tong andsChang []. Active learning enables the learner to use its own ability to respond to collect datasand to influence the world it is Today, robust learners multimedia in a real supervised machine learning application should count with a rich collection of positive and negative examples. Although in many applications, it is not difficult to obtain huge concept of data, labeling click the following article data can be a very expensive process, especially wh Although in many applications, it is not difficult to obtain huge amount of data, labeling those models can be a very expensive indexing, especially when dealing with data of high variability and complexity.
A good example of such theses are data from medical imaging applications where annotating anomalies like tumors, polyps, atherosclerotic plaque or informative frames in wireless endoscopy need highly trained experts. Building a representative set of thesis theses from medical videos e.
Wireless Capsule Endoscopy means that thousands of concepts to be labeled by an model. It is quite normal that data in new videos come different and thus are not represented by the concept set. In this model, we review the main approaches on active learning and illustrate how active learning can model to reduce expert effort in constructing the training sets.
We show that applying active thesis criteria, the number of human interventions can be significantly reduced. Several applications are known from the bibliography like: