An Explanatory Study on User Behavior in Discovering Aggregated Multimedia Web Content

Khan, Abdur Rehman; Rashid, Umer; Ahmed, Naveed

doi:10.1109/access.2022.3177597

Cited by 8 publications

(3 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proliferation of multimedia data creates challenges in accessing, interacting, and exploring massive multimedia resources [11], [42], [43]. However, fingerprinting is a nontrivial task that enhances human understanding of information resources via a smaller set of representatives identified as samples [44], [45].…”

Section: Multimedia Fingerprintsmentioning

confidence: 99%

“…Web users become overwhelmed with multimedia content, causing information overload, which hinders multimedia content exploration and access [8], [9]. Synthesizing vast multimedia resources with an abundance of different media formats and multiple information modalities via computing technologies is challenging [10], [11]. Ensuring users can access specific content from multimedia resources is a challenging endeavor [12].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Sampling Fingerprints From Multimedia Content Resource Clusters

Rashid,

Naseer,

Khan

et al. 2023

IEEE Access

Self Cite

View full text Add to dashboard Cite

Nowadays, the growth of multimedia content over the web is exponential. The fingerprints are inconspicuously embedded in multimedia content. The fingerprints can be exploited to trace divergent information from multimedia resources. Sampling fingerprints, particularly from multimedia resources, is challenging since they are complex, heterogeneous, and diverse. This research proposed an approach to sample fingerprints from multimedia resources. Our approach partitions the multimedia content space into converged clusters using variations of Canberra distance and identifies the most diverged samples using Kullback-Leibler (KL) divergence. The resultant clusters represent the information belonging to particular concepts and the diverged samples within the clusters represent multimedia fingerprints. The fingerprint sampling process is leveraged using unsupervised learning algorithms, instantiated across various multimedia descriptors, and tested over standard multimedia datasets. The average results obtained over various standard visual and acoustic datasets reveal 80%, 77%, and 78% accuracy, precision, and recall, respectively, surpassing most of the existing baseline clustering methods such as K-Means, Mean-Shift, and DBSCAN. Furthermore, the rigorousness of the proposed algorithm clustering is evaluated using the internal clustering stability silhouette coefficient and the fingerprint diversity scores. The results unveil a maximum of 94% diversity score. The proposed variation of Canberra distance and KL divergence provides the most stable performance (SD=0.02) and creates promising implications in future multimedia retrieval, summarization, and exploration activities.

show abstract

Section: Multimedia Fingerprintsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sampling Fingerprints From Multimedia Content Resource Clusters

Rashid,

Naseer,

Khan

et al. 2023

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…The exploration involves interaction with results presented via linear lists or grid layouts. The linear exploration mainly includes horizontal-vertical traversing [16]. The users can overview, preview, view, and access results.…”

Section: Introductionmentioning

confidence: 99%

An SUI-based approach to explore visual search results cluster-graphs

et al. 2023

View full text Add to dashboard Cite

Nowadays, exponential growth in online production and extensive perceptual power of visual contents (i.e., images) complicate the users’ information needs. The research has shown that users are interested in satisfying their visual information needs by accessing the image objects. However, the exploration of images via existing search engines is challenging. Mainly, existing search engines employ linear lists or grid layouts, sorted in descending order of relevancy to the user’s query to present the image results, which hinders image exploration via multiple information modalities associated with them. Furthermore, results at lower-ranking positions are cumbersome to reach. This research proposed a Search User Interface (SUI) approach to instantiate the non-linear reachability of the image results by enabling interactive exploration and visualization options. We represent the results in a cluster-graph data model, where the nodes represent images and the edges are multimodal similarity relationships. The results in clusters are reachable via multimodal similarity relationships. We instantiated the proposed approach over a real dataset of images and evaluated it via multiple types of usability tests and behavioral analysis techniques. The usability testing reveals good satisfaction (76.83%) and usability (83.73%) scores.

show abstract