RELIEF-MM: effective modality weighting for multimedia information retrieval

Yilmaz, Turgay
Yazıcı, Adnan
Kitsuregawa, Masaru
Fusing multimodal information in multimedia data usually improves the retrieval performance. One of the major issues in multimodal fusion is how to determine the best modalities. To combine the modalities more effectively, we propose a RELIEF-based modality weighting approach, named as RELIEF-MM. The original RELIEF algorithm is extended for weaknesses in several major issues: class-specific feature selection, complexities with multi-labeled data and noise, handling unbalanced datasets, and using the algorithm with classifier predictions. RELIEF-MM employs an improved weight estimation function, which exploits the representation and reliability capabilities of modalities, as well as the discrimination capability, without any increase in the computational complexity. The comprehensive experiments conducted on TRECVID 2007, TRECVID 2008 and CCV datasets validate RELIEF-MM as an efficient, accurate and robust way of modality weighting for multimedia data.


Graph-based multilevel temporal video segmentation
Sakarya, Ufuk; TELATAR, ZİYA (Springer Science and Business Media LLC, 2008-11-01)
This paper presents a graph-based multilevel temporal video segmentation method. In each level of the segmentation, a weighted undirected graph structure is implemented. The graph is partitioned into clusters which represent the segments of a video. Three low-level features are used in the calculation of temporal segments' similarities: visual content, motion content and shot duration. Our strength factor approach contributes to the results by improving the efficiency of the proposed method. Experiments sho...
Multimodal concept detection in broadcast media: KavTan
SOYSAL, Medeni; Alatan, Abdullah Aydın; TEKİN, Mashar; ESEN, Ersin; SARACOĞLU, Ahmet; Acar, Banu Oskay; Ozan, Ezgi Can; Ates, Tugrul K.; SEVİMLİ, Hakan; SEVİNÇ, Muge; ATIL, Ilkay; Ozkan, Savas; Arabaci, Mehmet Ali; TANKIZ, Seda; KARADENİZ, Talha; ÖNÜR, Duygu; SELÇUK, Sezin; Alatan, A. Aydin; Çiloğlu, Tolga (Springer Science and Business Media LLC, 2014-10-01)
Concept detection stands as an important problem for efficient indexing and retrieval in large video archives. In this work, the KavTan System, which performs high-level semantic classification in one of the largest TV archives of Turkey, is presented. In this system, concept detection is performed using generalized visual and audio concept detection modules that are supported by video text detection, audio keyword spotting and specialized audio-visual semantic detection components. The performance of the p...
Event prediction from news text using subgraph embedding and graph sequence mining
Çekinel, Recep Fırat; Karagöz, Pınar (Springer Science and Business Media LLC, 2022-2-28)
Event detection from textual content by using text mining concepts is a well-researched field in the literature. On the other hand, graph modeling and graph embedding techniques in recent years provide an opportunity to represent textual contents as graphs. Text can be enriched with additional attributes in graphs, and the complex relationships can be captured within the graphs. In this paper, we focus on news prediction and model the problem as subgraph prediction. More specifically, we aim to predict the ...
Mobility and power aware data interest based data replication for mobile ad hoc networks
Arslan, Seçil; Bozyiğit, Müslim; Department of Computer Engineering (2007)
One of the challenging issues for mobile ad hoc network (MANET) applications is data replication. Unreliable wireless communication, mobility of network participators and limited resource capacities of mobile devices make conventional replication techniques useless for MANETs. Frequent network divisions and unexpected disconnections should be handled. In this thesis work, a novel mobility and power aware, data interest based data replication strategy is presented. Main objective is to improve data accessibi...
Efficiency and effectiveness of query processing in cluster-based retrieval
Can, F; Altıngövde, İsmail Sengör; Demir, E (Elsevier BV, 2004-12-01)
Our research shows that for large databases, without considerable additional storage overhead, cluster-based retrieval (CBR) can compete with the time efficiency and effectiveness of the inverted index-based full search (FS). The proposed CBR method employs a storage structure that blends the cluster membership information into the inverted file posting lists. This approach significantly reduces the cost of similarity calculations for document ranking during query processing and improves efficiency. For exa...
Citation Formats
T. Yilmaz, A. Yazıcı, and M. Kitsuregawa, “RELIEF-MM: effective modality weighting for multimedia information retrieval,” MULTIMEDIA SYSTEMS, pp. 389–413, 2014, Accessed: 00, 2020. [Online]. Available: