Revisiting SIRET Video Retrieval Tool

Lokoč, Jakub; Kovalčík, Gregor; Souček, Tomáš

doi:10.1007/978-3-319-73600-6_44

Cited by 13 publications

(9 citation statements)

References 12 publications

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“…), (ii) the data indexing and search mechanisms used at the core of the system, (iii) the techniques employed during video preprocessing to automatically annotate selected keyframes and extract image features, (iv) the functionalities integrated into the user interface, including advanced visualization and relevance feedback. Among all the systems that participated in VBS, we recall VIRET [ 25 ], vitrivr [ 26 ], and SOM-Hunter [ 27 ], which won the competition in 2018, 2019, and 2020, respectively.…”

Section: Related Workmentioning

confidence: 99%

The VISIONE Video Search System: Exploiting Off-the-Shelf Text Search Engines for Large-Scale Video Retrieval

et al. 2021

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This paper describes in detail VISIONE, a video search system that allows users to search for videos using textual keywords, the occurrence of objects and their spatial relationships, the occurrence of colors and their spatial relationships, and image similarity. These modalities can be combined together to express complex queries and meet users’ needs. The peculiarity of our approach is that we encode all information extracted from the keyframes, such as visual deep features, tags, color and object locations, using a convenient textual encoding that is indexed in a single text retrieval engine. This offers great flexibility when results corresponding to various parts of the query (visual, text and locations) need to be merged. In addition, we report an extensive analysis of the retrieval performance of the system, using the query logs generated during the Video Browser Showdown (VBS) 2019 competition. This allowed us to fine-tune the system by choosing the optimal parameters and strategies from those we tested.

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Section: Related Workmentioning

confidence: 99%

The VISIONE Video Search System: Exploiting Off-the-Shelf Text Search Engines for Large-Scale Video Retrieval

et al. 2021

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“…Figure 4 depicts the winning systems over all interations of the VBS so far. It can be seen that the interface complexity is not dramatically different for the most recent winning systems (e.g., vitrivr [7] and VIRET [8]) in comparison to the winning systems of 2012 and 2013 (ITEC [14] and NII-UIT [13]). However, as can be expected, the complexity of earlier VBS systems is significantly different to the most recent ones.…”

Section: Vbs Setup and Evaluation Servermentioning

confidence: 99%

Video Browser Showdown 2012-2019: A Review

Schoeffmann

2019

2019 International Conference on Content-Based Multimedia Indexing (CBMI)

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The Video Browser Showdown (VBS) is a challenging video retrieval competition annually performed at the International Conference on MultiMedia Modeling (MMM). This paper presents a review of all eight years of the VBS performed so far-as a summary of the keynote presented at CBMI 2019. It discusses the different types of tasks and sessions, provides an overview of the datasets and participating teams, and it gives some insights on the setup and evaluation procedure. It also evaluates the progress made over the last few years in terms of average task solve time and task solve ratio.

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“…Serving as the epicenter of research on interactive multimedia retrieval, the initiatives such as Video Browser Showdown produced a number of excellent analytics systems [13]. [24] GraphViz [9] PIWI [35] Newdle [36] Gephi [2] CoMeRDA [5] Blackthorn [38] vitrivr [20] SIRET [12] Vibro [1] PICTuReVis [29] ISOLDE For example, vitrivr system owes it good performance in interactive multimedia retrieval to an indexing structure for efficient kNN search [20]. Similarly, SIRET tool facilitates interactive video retrieval using several querying strategies, i.e.…”

Section: Multimedia Analyticsmentioning

confidence: 99%

“…Similarly, SIRET tool facilitates interactive video retrieval using several querying strategies, i.e. query by keywords, query by color-sketch and query by example image [12]. Following a different approach, Vibro system relies on a hierarchical graph structure for interactive exploration of large video collections [1].…”

Section: Multimedia Analyticsmentioning

confidence: 99%

Interactive Search and Exploration in Discussion Forums Using Multimodal Embeddings

Gornishka

Rudinac

Worring

2019

MultiMedia Modeling

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In this paper we present a novel interactive multimodal learning system, which facilitates search and exploration in large networks of social multimedia users. It allows the analyst to identify and select users of interest, and to find similar users in an interactive learning setting. Our approach is based on novel multimodal representations of users, words and concepts, which we simultaneously learn by deploying a general-purpose neural embedding model. We show these representations to be useful not only for categorizing users, but also for automatically generating user and community profiles. Inspired by traditional summarization approaches, we create the profiles by selecting diverse and representative content from all available modalities, i.e. the text, image and user modality. The usefulness of the approach is evaluated using artificial actors, which simulate user behavior in a relevance feedback scenario. Multiple experiments were conducted in order to evaluate the quality of our multimodal representations, to compare different embedding strategies, and to determine the importance of different modalities. We demonstrate the capabilities of the proposed approach on two different multimedia collections originating from the violent online extremism forum Stormfront and the microblogging platform Twitter, which are particularly interesting due to the high semantic level of the discussions they feature. CCS CONCEPTS• Information systems → Multimedia and multimodal retrieval. KEYWORDS multimedia analytics, search, exploration, interactive learning, multimodal embeddings, online discussion forums, social multimedia • First, compact but meaningful multimodal content representations are needed to ensure the interactivity of the system

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Revisiting SIRET Video Retrieval Tool

Cited by 13 publications

References 12 publications

The VISIONE Video Search System: Exploiting Off-the-Shelf Text Search Engines for Large-Scale Video Retrieval

The VISIONE Video Search System: Exploiting Off-the-Shelf Text Search Engines for Large-Scale Video Retrieval

Video Browser Showdown 2012-2019: A Review

Interactive Search and Exploration in Discussion Forums Using Multimodal Embeddings

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