Generalized visual concept detection
Ahmet Saracoğlu, Mashar Tekin, Ersin Esen, Medeni Soysal, K. Berker Loğoğlu, Tuğrul K. Ateş, A. Muge Sevinc, Hakan Sevimli, Banu Oskay Acar, Ünal Zubari, Ezgi Can Ozan, A. Aydi Alatan · 2010
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.