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A Novel Prototype for Documentation and retrieval of 3D Objects

A Novel Prototype for Documentation and Retrieval of 3D Obje...

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International conference on image Analysis and Processing Workshops (ICIAPW)

作者： Efstathios Onasoglou Athanasios Mademlis Dimitrios Katsikas Petros Daras Michael Strintzis Informatics and Telematics Institute Centre for Research and Technology Hellas Greece Altanet SA Thessaloniki Greece Information Processing Labaratory Department of Electrical & Computer Engineering Aristotle University of Thessaloniki Greece

In this paper a working multimedia asset management system is presented which provides the means for storage, annotation, retrieval and re-use of any multimedia data such as 3D objects, images, sound, video and text. The working prototype is modular, customizable, secure, offers intranet and Internet connectivity and also software inter-operability as it can be compatible with various well- known, established database and ontology standards. The main innovative aspects of the proposed system are the efficient combination of the semantic aspects with multimedia data and the retrieval engine of the system which combines state-of-the-art 3D content-based algorithms and novel semantic driven, relevance feedback methods.

关键词： Prototypes Documentation Multimedia systems image storage Information retrieval Multimedia databases Asset management image retrieval Software prototyping Internet

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Dimensionality reduction for similarity searching in dynamic databases

Dimensionality reduction for similarity searching in dynamic...

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Proceedings of the ACM SIGMOD International conference on Management of Data

作者： Kanth, K.V.Ravi Agrawal, Divyakant Singh, Ambuj Univ of California at Santa Barbara Santa Barbara United States

databases are increasingly being used to store multi-media objects such as maps, images, audio and video. storage and retrieval of these objects is accomplished using multi-dimensional index structures such as R*-trees and SS-trees. As dimensionality increases, query performance in these index structures degrades. This phenomenon, generally referred to as the dimensionality curse, can be circumvented by reducing the dimensionality of the data. Such a reduction is however accompanied by a loss of precision of query results. Current techniques such as QBIC use SVD transform-based dimensionality reduction to ensure high query precision. The drawback of this approach is that SVD is expensive to compute, and therefore not readily applicable to dynamic databases. In this paper, we propose novel techniques for performing SVD-based dimensionality reduction in dynamic databases. When the data distribution changes considerably so as to degrade query precision, we recompute the SVD transform and incorporate it in the existing index structure. For recomputing the SVD-transform, we propose a novel technique that uses aggregate data from the existing index rather than the entire data. This technique reduces the SVD-computation time without compromising query precision. We then explore efficient ways to incorporate the recomputed SVD-transform in the existing index structure without degrading subsequent query response times. These techniques reduce the computation time by a factor of 20 in experiments on color and texture image vectors. The error due to approximate computation of SVD is less than 10%.

关键词： Distributed database systems

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Using feature selection to aid an iconic search through an image database

Using feature selection to aid an iconic search through an i...

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International conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： K. Messer J. Kittler University of Surrey Guildford Surrey UK

In this paper a method that facilitates an iconic query of an image/video database is presented. A query object is characterised by colour and texture properties. The same characteristics are computed locally for the database images. A statistical decision rule is then used to test for similarity between the iconically specified query and the database image descriptors. We show that by carefully selecting the set of descriptors the false alarm rate can be significantly reduced. The floating search feature selection method has been adapted to make it applicable to the hypothesis testing based query processing. The dimensionality reduction not only improves the performance but also enhances the computational efficiency of the method.

关键词： image databases Spatial databases Shape Testing Indexing Information retrieval image storage Humans image retrieval World Wide Web

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An automatic analyzer for sports video databases using visual cues and real-world modeling

An automatic analyzer for sports video databases using visua...

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IEEE International conference on Consumer Electronics (ICCE)

作者： Jungong Han D. Farin P.H.N. de With Weilun Lao Eindhovan University of Technology Eindhoven Netherlands

With the advent of hard-disk video recording, video databases gradually emerge for consumer applications. The large capacity of disks requires the need for fast storage and retrieval functions. We propose a semantic analyzer for sports video, which is able to automatically extract and analyze key events, such as player behavior. The analyzer employs several visual cues and a model for real-world coordinates, so that speed and position of a player can be determined with sufficient accuracy. It consists of four processing steps: (1) playing event detection, (2) court and player segmentation, as well as a 3-D camera model, (3) player tracking, and (4) event-based high-level analysis exploiting visual cues extracted in the real-world. We show attractive experimental results remarking the system efficiency and classification skills.

关键词： Visual databases Event detection image segmentation Games Performance analysis video recording Layout image analysis Cameras Organizing

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Spatio-temporal feature based VLAD for efficient video retrieval

Spatio-temporal feature based VLAD for efficient video retri...

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National conference on Computer Vision, Pattern Recognition, image Processing and Graphics (NCVPRIPG)

作者： Mopuri K Reddy Sahil Arora R. Venkatesh Babu Video Analytics Lab SERC Indian Institute of Science India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India

Compact representation of visual content has emerged as an important topic in the context of large scale image/video retrieval. The recently proposed Vector of Locally Aggregated Descriptors (VLAD) has shown to outperform other existing techniques for retrieval. In this paper, we propose two spatio-temporal features for constructing VLAD vectors for videos in the context of large scale video retrieval. Given a particular query video, our aim is to retrieve similar videos from the database. Experiments are conducted on UCF50 and HMDB51 datasets, which pose challenges in the form of camera motion, view-point variation, large intra-class variation, etc. The paper proposes the following two spatio-temporal features for constructing VLADs i) Local Histogram of Oriented Optical Flow (LHOOF), and ii) Space-Time Invariant Points (STIP). The performance of these proposed features are compared with SIFT based spatial feature. The mean average precision (MAP) indicates the better retrieval performance of the proposed spatio-temporal feature over spatial feature.

关键词： Feature extraction Vectors Computer vision Histograms Optical imaging databases conferences

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ICSVR: Investigating Compositional and Syntactic Understanding in video retrieval Models

ICSVR: Investigating Compositional and Syntactic Understandi...

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IEEE Computer Society conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Avinash Madasu Vasudev Lal Cognitive AI Intel Labs

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

video retrieval (VR) involves retrieving the ground truth video from the video database given a text caption or vice-versa. The two important components of compositionality: objects & attributes and actions are joined using correct syntax to form a proper text query. These components (objects & attributes, actions and syntax) each play an important role to help distinguish among videos and retrieve the correct ground truth video. However, it is unclear what is the effect of these components on the video retrieval performance. We therefore, conduct a systematic study to evaluate the compositional and syntactic understanding of video retrieval models on standard benchmarks such as MSRVTT, MSVD and DIDEMO. The study is performed on two categories of video retrieval models: (i) which are pre-trained on video-text pairs and fine-tuned on downstream video retrieval datasets (Eg. Frozen-in-Time, Violet, MCQ etc.) (ii) which adapt pre-trained image-text representations like CLIP for video retrieval (Eg. CLIP4Clip, XCLIP, CLIP2video etc.). Our experiments reveal that actions and syntax play a minor role compared to objects & attributes in video understanding. Moreover, video retrieval models that use pre-trained image-text representations (CLIP) have better syntactic and compositional understanding as compared to models pre-trained on video-text data. The code is available at https://***/IntelLabs/multimodal_cognitive_ai/tree/main/ICSVR.

关键词： Adaptation models Systematics databases Computational modeling conferences Syntactics Benchmark testing

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An exhaustive study on data mining techniques in mining of Multimedia database

An exhaustive study on data mining techniques in mining of M...

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International conference on Issues and Challenges in Intelligent Computing Techniques (ICICT)

作者： Pramod Kumar Yadav Sam Rizvi Dept. of Computer Science & Engineering Krishna Institute of Engineering & Technology Ghaziabad Dept. of Computer Science Jamia Millia Islamia University New Delhi

Multimedia database can be define as a collection of storage and retrieval systems, in which large amount of media objects are created, modified, searched and retrieved, where as Multimedia is the combination of text, image, graphics, animations, audio and video. The extension of database application to handle multimedia objects requires synchronization of multiple media data streams. Multimedia data mining refers to the extraction of implicit knowledge, data relationships, or other patterns which are not stored in multimedia files explicitly. The system's overall performance in retrieval can be increase by indexing and classification of multimedia data with efficient information fusion of the different modalities is mandatory. Apart from text retrieval, the current waves in web searching and multimedia data retrieval are the search for and delivery of 3D scenes, images, music and video. The content-based multimedia information retrieval provides new techniques and methods for searching various multimedia databases over the world.

关键词： Data mining databases Analytical models Multimedia communication Streaming media Lead

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Effective ways for querying images by content over the Internet

Effective ways for querying images by content over the Inter...

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Mediterranean Electrotechnical conference (MELECON)

作者： M. Vlachos G. Vardangalos C. Tatsiopoulos Epsilon Software SA Athens Greece Department of Electrical and Computer Engineering Division of Computer Science National and Technical University of Athens Greece Senior Software Engineer Athens Greece

Increasing numbers of images have made explicit the need for more effective and efficient image indexing and searching of not only the meta-data associated with an image but also retrieval directly based on the content of the image. The scope of the COBWEB (COntext-Based image retrieval on the WEB) project (EP28773) is to create a pioneer system that provides a solution to the problem of automatically storing and retrieving images from large image databases dispersed over the Internet. The project focuses on the following issues. (i) Development of effective algorithms for automatic storage of the visual features of the images, which can be used as a means for retrieving similar images. (ii) Creation of a simple, cross-platform user interface that helps the user to query the database in the most natural way. (iii) Development of a robust communication subsystem that guarantees satisfying retrieval times. In this paper, we describe the architecture and implementation details of the human-computer interface (HCI) and the intelligent search component (ISC), which are two of the main components of the COBWEB system. The HCI consists of a graphical interface, which enables the user to interact with the system. It provides the user with various approaches in querying the database, including keyword specification and sample image submission. The ISC's main tasks are to handle and translate the query criteria submitted by the user through the HCI, to navigate the database and find images that satisfy those criteria, and to return the results to the HCI.

关键词： Internet image retrieval Information retrieval image databases Human computer interaction image storage Indexing Content based retrieval storage automation User interfaces

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User friendly approach for video search technique using text and image as query

User friendly approach for video search technique using text...

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conference on IT in Business, Industry and Government (CSIBIG)

作者： Vishakha Soni Sandeep Kumar Mathariya Ranu Soni Indore Institute of Science and Technology - II Indore M P Sri Aurobindo Institute of Technology Indore M P. R. C. Patel Institute of Technology shirpur M H

With the evolution of Internet and Computer Technology, the digital videos are increasing explosively. There's an enormous number of videos available online. So how to get the interesting video clips from the massive video dataset quickly and efficiently has become an urgent problem in the field of content-based information retrieval. Content-Based searching and retrieval of video data has become a challenging and important issue among the search engines. Generally videos are retrieved from a large collection of videos based on the keyword which is only text. "Multimodal Fusion for video Search Re-ranking" is a flexible and effective reranking method, and it is also called CR-Reranking, to improve the retrieval effectiveness. To offer high accuracy on the top-ranked results, CRRe-ranking employs a cross-reference (CR) strategy to fuse multimodal cues. Given a text query by users, the system then returns a series of approximately relevant video shots of matching the input text with the text documents associated with the video shots. But the results of this method are less relevant as the video contains only text in the title. Also this method has time overhead. In the proposed system, videos are retrieved not only through keywords but also using an image as a query. Users are usually interested in the top ranked portion of the returned search results and therefore it is crucial for search engines to achieve accuracy on the search results. Content based video searching and retrieval framework is proposed to improve the efficiency and accuracy of the search engines. The input is given as a video at the training time, where the video is converted into images. At the time of searching, user gives a text or an image as a query to the system which then returns a series of additional relevant video shots than the previous methods by mapping with the images. At the time of training, the user can associate some relevant text to the extracted images and then some query image c

关键词： Feature extraction Accuracy image databases Search engines image color analysis

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A New Method for image Classification by Using Multilevel Association Rules

A New Method for Image Classification by Using Multilevel As...

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International conference on Data Engineering Workshops

作者： V.S. Tseng Ming-Hsiang Wang Ja-Hwung Su Institute of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan

With the popularity of multimedia applications, the huge amount of image and video related to real life have led to the proliferation of emerging storage techniques. Contented-based image retrieval and classification have become attractive issues in the last few years. Most researches concerning image classification focus primarily on low-level image features (e.g. color, texture, shape, etc.) and ignore the conceptual associations among the objects in the images. In this paper, we propose a new image classification method by using multiple-level association rules based on the image objects. The approach we proposed can be decomposed of three phases: (1) building of conceptual object hierarchy, (2) discovery of classification rules, and (3) classification and prediction of images. At the first phase, we use a hierarchical clustering method to build the conceptual hierarchy based on the low-level features of image objects. At the second phase, we devise a multi-level mining algorithm for finding the image classification rules. The classification task is performed at the last phase. Empirical evaluations show that our approach performs better than other approaches in terms of classification accuracy.

关键词： image classification Association rules Multimedia databases Data mining Information retrieval image retrieval Content based retrieval Feature extraction image segmentation image storage

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