Generalized visual concept detection

For efficient indexing and retrieval of video archives, concept detection stands as an important problem. In this work, a generalized structure that can be used for detection of diverse and distinct concepts is proposed. In the system, MPEG-7 Descriptors and Scale Invariant Transform (SIFT) are util...

Full description

Saved in:
Bibliographic Details
Main Authors: Saracoğlu, Ahmet, Tekin, M, Esen, E, Soysal, M, Loğoğlu, K Berker, Ateş, Tuğrul K, Sevinç, A Müge, Sevimli, H, Acar, B O, Zubari, Ünal, Ozan, E C, Alatan, A A
Format: Conference Proceeding
Language:English
Subjects:
Feature extraction
Histograms
Indexing
Semantics
Support vector machines
Transform coding
Visualization
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:For efficient indexing and retrieval of video archives, concept detection stands as an important problem. In this work, a generalized structure that can be used for detection of diverse and distinct concepts is proposed. In the system, MPEG-7 Descriptors and Scale Invariant Transform (SIFT) are utilized as visual features. Furthermore, visual features are transformed by codebooks which are constructed by k-Means clustering. On the other hand, classification is performed on the distribution of visual features over the codebook. Proposed system is firstly tested against an elementary concept. Afterwards for a set of concepts system performance is reported on the TRECVID 2009 test set. It has been observed that with a sufficiently large training set high performance can be achieved with this method.
ISSN:2165-0608
2693-3616
DOI:10.1109/SIU.2010.5650360