Content-based query processing for video databases

Kuo, Tony C. T.; Chen, A.L.P.

doi:10.1109/6046.825790

Cited by 44 publications

(31 citation statements)

References 21 publications

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“…It is usually decomposed into some sub-queries which in fact can be regarded a combination of the low-level query and the spatialtemporal query [1] [83]. With these query types, many systems have been proposed in the past, such as OVID [77], QBIC [33], AVIS [1], CVQL [57] and BilVideo [103]. We notice that some of them also design index structure for the enhanced query type.…”

Section: Related Workmentioning

confidence: 99%

“…A content-based video query language (CVQL) is proposed in [57]. CVQL treats the objects as points and express the spatial relations by distance or motion.…”

Section: Frame-level Filteringmentioning

confidence: 99%

“…There is one major limitation that all the existing works [57,22,63,49] have. That is, the construction process of these two tables heavily depends on human labors [23,53].…”

Section: Introductionmentioning

confidence: 99%

“…To manage the visual objects for frame-level filtering, existing works usually maintain two tables simultaneously as Figure 3.1: one is the unique object table which stores the globally identified objects appear in the videos, the other one is the occurrence table which stores the occurrences of the globally identified objects in the videos [57,22,63,49]. Ideally, all the occurrences should be connected to the identified objects.…”

Section: Introductionmentioning

confidence: 99%

“…This requires all the visual objects are annotated in advance, which is infeasible at present. Existing works [57,24,115] employ human to obtain the visual objects on each frame. As the growth rate of videos is unprecedentedly high, the manually annotated visual objects are usually scarce on large-scale or dynamic data.…”

Section: Introductionmentioning

confidence: 99%

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Multi-level Video Filtering Using Non-textual Contents

Du¹

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Video is one of the major media human uses to store information. As the recording and storing devices become cheaper, there are numerous videos generated nowadays. The unprecedentedly large volume creates considerable new requirements on accessing the videos. Therefore, how to perform video filtering, i.e. obtaining a set of relevant video clips from the video repository becomes a challenging research topic. In previous works, video filtering required user entering some texts to filter the irrelevant video clips, which made the video filtering methods same as the document filtering methods for a long time. However, there are three limitations of the text-based video filtering: (1) it dismisses the rich contents in the videos; (2) it is inapplicable when the texts are absent, incomplete or sparse; (3) it fails to support in-video filtering. These limitations make the text-based video filtering powerless after the new requirements emerge. In recent years, there sees a tendency that computer could parse more meaningful contents from the videos. These non-textual contents are complementary to the texts in many cases. Enlightened by that, existing video filtering research gradually shifts from text-based to non-textual-based. Under this direction, we study how to improve the video filtering systematically from three levels.Frame-level. We propose to use detected visual object to filter the videos. In previous works, the visual objects were obtained manually where human took the responsibility of identifying the visual objects and connecting them in the videos. The process of obtaining the visual objects is costly when the data keep changing. Therefore, we proposed to leverage the object detection to obtain the visual objects automatically for frame-level filtering. However, object detection itself is unable to identify and connect the visual objects like human. To achieve that, we proposed a hybrid method to identify and connect the visual objects, which is further divided into local merge, propagation and global merge. We examined the proposed method on a real-world dataset then studied two issues:(1) whether the identifications and connections were accurate, as well as (2) how the environment influenced the proposed method. The experimental results were promising and proved that using detected visual objects for frame-level filtering is feasible.Video-level. We discover a new small content set for surveillance video filtering. Surveillance video filtering, namely surveillance event detection (SED), is important for many safety and security applications. It aims to alarm the events from the surveillance videos. Different from classical video filtering which extracts video content vectors from diverse sources, SED is only able to leverage the motion contents. And the state-of-the-art content set for surveillance is made up of STIP and MoSIFT.iii In our study, we proposed a new content set by using dense trajectory (DT) and improved dense trajectory (IDT). According to our analysis, our new content set capture...

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Section: Related Workmentioning

confidence: 99%

“…A content-based video query language (CVQL) is proposed in [57]. CVQL treats the objects as points and express the spatial relations by distance or motion.…”

Section: Frame-level Filteringmentioning

confidence: 99%

“…There is one major limitation that all the existing works [57,22,63,49] have. That is, the construction process of these two tables heavily depends on human labors [23,53].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-level Video Filtering Using Non-textual Contents

Du¹

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Flexible Querying Using Structural and Event Based Multimodal Video Data Model

Oztarak

Yazıcı

2006

Flexible Query Answering Systems

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Structural and Event Based Multimodal Video Data Modeling

Oztarak

Yazıcı

2006

Advances in Information Systems

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Investments on multimedia technology enable us to store many more reflections of the real world in digital world as videos. By recording videos about real world entities, we carry a lot of information to the digital world directly. In order to store and efficiently query this information, a video database system (VDBS) is necessary. In this thesis work, we propose a structural, event based and multimodal (SEBM) video data model for VDBSs. SEBM video data model supports three different modalities that are visual, auditory and textual modalities and we propose that we can dissolve these three modalities with a single SEBM video data model. This proposal is supported by the interpretation of the video data by human. Hence we can answer the content based, spatio-temporal and fuzzy queries of the user more easily, since we store the video data as the way that s/he interprets the real world data. We follow divide and conquer technique when answering very complicated queries. We have implemented the SEBM video data model in a Java based system that uses XML for representing the SEBM data model and Berkeley XML DBMS for storing the data based on the SEBM prototype system.

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Content-based query processing for video databases

Cited by 44 publications

References 21 publications

Multi-level Video Filtering Using Non-textual Contents

Multi-level Video Filtering Using Non-textual Contents

Flexible Querying Using Structural and Event Based Multimodal Video Data Model

Structural and Event Based Multimodal Video Data Modeling

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