Enhancing sketch-based sport video retrieval by suggesting relevant motion paths

Kabary, Ihab Al; Schuldt, Heiko

doi:10.1145/2600428.2609551

Cited by 11 publications

(3 citation statements)

References 10 publications

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“…Besides, several computational methods have been appended to the conventional query expansion, which includes (a) generating pseudo-relevant documents, (b) establishing the expanded query by deriving the most relevant codeword from the generated pseudo-relevant documents, and (c) using the expanded query to retrieve the most precise documents. Due to the simplicity of the query expansion technique, it has been used in many applications such as e-commerce, 4 information filtering, 5 sports and games, 6 and medical. 7 A metaheuristic is a high-level problem-oriented structure 8,9 used to evolve the heuristic optimization algorithm by considering the set of procedures provided by it.…”

Section: Introductionmentioning

confidence: 99%

An automation query expansion strategy for information retrieval by using fuzzy based grasshopper optimization algorithm on medical datasets

Srivel¹,

Kalaiselvi

Shanthi

et al. 2022

Concurrency and Computation

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Due to the technical words employed, which are primarily recognized by medical specialists, information retrieval in the medical area is sometimes described as sophisticated. Because of this, users frequently have trouble coming up with queries utilizing these medical phrases. However, this problem may be readily fixed by an information retrieval system that finds the pertinent terms that fit the user's query and automatically creates a ranking document using these keywords. To enhance the IR performance, the Automatic Query expansion method is applied by appending additional query terms for the medical domain. We propose a novel fuzzy-based Grasshopper Optimization Algorithm (GOA) based on automatic query expansion. This work is mainly focused on filtering the most relevant augmented query by utilizing the synchronization score of IR evidence like normalized term frequency, inverse document frequency, and normalization of document length. The main aim of this work is to identify the medical terms that appropriately match the user's queries. The GOA algorithm ranks the terms based on relevance and then identifies the terms with the maximum synchronization value. The documents formed using the optimal expanded query are classified into three types, namely totally relevant, moderately relevant, and marginally relevant. Besides, the comparison of the proposed work is carried out for different performance metrics like Mean-Average Precision, F-measure, Precision-recall, and Precision rank are evaluated and analyzed by using TREC-COVID, TREC Genomics 2007, and MEDLARs medical datasets for the proposed and some of the state-of-art works. For a total of 60 queries, the proposed model offers an F1-Score of 0.964, 0.959, and 0.968 for the MEDLARS, TREC Genomics, and TREC COVID19 datasets, respectively. The E1-score and Mean Reciprocal Rate (MRR) of the proposed model is 0.8 and 0.9 when evaluated using the TREC COVID19 dataset. Performance analyses show that the proposed approach outperforms the other automatic keyword expansion approaches in the medical domain.

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Section: Introductionmentioning

confidence: 99%

An automation query expansion strategy for information retrieval by using fuzzy based grasshopper optimization algorithm on medical datasets

Srivel¹,

Kalaiselvi

Shanthi

et al. 2022

Concurrency and Computation

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“…For instance, for standard information retrieval systems, only one step is needed: (i) retrieve the relevant documents to the initial query; whereas, for QE, three steps are required: (i) retrieve the pseudo-relevant documents to the initial query, (ii) extract the best expansion keywords from the pseudorelevant documents to constitute the expanded query, and finally (iii) retrieve the relevant documents to the expanded query. Query expansion is currently used in various applications such as multimedia information retrieval [24], question answering [31], information filtering [22]; and applied to several domains such as sport and game [1], medical and health [9,30] and e-commerce [38].…”

Section: Introductionmentioning

confidence: 99%

An accelerated PSO for query expansion in web information retrieval: application to medical dataset

Khennak

Drias

2017

Appl Intell

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Swarm intelligence algorithms are now among the most widely used soft computing techniques for optimization and computational intelligence. One recent swarm intelligence algorithm that has begun to receive more attention is Accelerated Particle Swarm Optimization (APSO). It is an enhanced version of PSO with global optimization capability, sufficient simplicity and high flexibility. In this paper, we propose the application of the APSO technique to efficiently solve the problem of Query Expansion (QE) in Web Information Retrieval (IR). Unlike prior studies, we introduce a new modelling of QE that aims to find the suitable expanded query from among a set of expanded query candidates. Nevertheless, due to the large number of potential expanded query candidates, it is extremely complex to produce the best one through conventional hard computing methods. Therefore, we propose to consider the problem of QE as a combinatorial optimization problem and address it with APSO. We thoroughly evaluate the proposed APSO for QE using MEDLINE, the world Web's largest medical library. We first conduct a preliminary experiment to tune the APSO parameters. Then, we compare the results to a recent swarm intelligence algorithm called Firefly Algorithm (FA). We also compare the results with three recently published methods for QE that involved Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Bat Algorithm (BA). The experimental analysis demonstrates that the proposed APSO for QE is very competitive and yields substantial improvement over the other methods in terms of retrieval effectiveness and computational complexity.

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“…The semantic annotations obtained from the CrowdSport application are later on used as basis for sophisticated retrieval functionality leveraging the team's collective behavior and/or the motion of individuals that is offered to game analysts and coaches [1,2]. For instance, the data gained from the CrowdSport application is used in [1] for retrieving video scenes for which the user specifies e.g., a location or a motion path.…”

Section: Introductionmentioning

confidence: 99%

Crowd-based Semantic Event Detection and Video Annotation for Sports Videos

Sulser

Giangreco

Schuldt

2014

Proceedings of the 2014 International ACM Workshop on Crowdsourcing for Multimedia

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Recent developments in sport analytics have heightened the interest in collecting data on the behavior of individuals and of the entire team in sports events. Rather than using dedicated sensors for recording the data, the detection of semantic events reflecting a team's behavior and the subsequent annotation of video data is nowadays mostly performed by paid experts. In this paper, we present an approach to generating such annotations by leveraging the wisdom of the crowd. We present the CrowdSport application that allows to collect data for soccer games. It presents crowd workers short video snippets of soccer matches and allows them to annotate these snippets with event information. Finally, the various annotations collected from the crowd are automatically disambiguated and integrated into a coherent data set.To improve the quality of the data entered, we have implemented a rating system that assigns each worker a trustworthiness score denoting the confidence towards newly entered data. Using the DBSCAN clustering algorithm and the confidence score, the integration ensures that the generated event labels are of high quality, despite of the heterogeneity of the participating workers. These annotations finally serve as a basis for a video retrieval system that allows users to search for video sequences on the basis of a graphical specification of team behavior or motion of the individual player. Our evaluations of the crowd-based semantic event detection and video annotation using the Microworkers platform have shown the effectiveness of the approach and have led to results that are in most cases close to the ground truth and can successfully be used for various retrieval tasks.

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Enhancing sketch-based sport video retrieval by suggesting relevant motion paths

Cited by 11 publications

References 10 publications

An automation query expansion strategy for information retrieval by using fuzzy based grasshopper optimization algorithm on medical datasets

An automation query expansion strategy for information retrieval by using fuzzy based grasshopper optimization algorithm on medical datasets

An accelerated PSO for query expansion in web information retrieval: application to medical dataset

Crowd-based Semantic Event Detection and Video Annotation for Sports Videos

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