This paper presents an approach on high-level feature detection using a region thesaurus. MPEG-7 features are locally extracted from segmented regions and for a large set of images. A hierarchical clustering approach is applied and a relatively small number of region types is selected. This set of region types defines the region thesaurus. Using this thesaurus, low-level features are mapped to high-level concepts as model vectors. This representation is then used to train support vector machine-based feature detectors. As a next step, latent semantic analysis is applied on the model vectors, to further improve the analysis performance. High-level concepts detected derive from the natural disaster domain.

/pdf/a-region-thesaurus-approach-for-high-level-concept-detection-4axgdbobol.pdf

A region thesaurus approach for high-level concept detection in the natural disaster domain

This paper presents a framework for the detection of semantic features in video sequences. Low-level feature extraction is performed on the keyframes of the shots and a “feature vector” including color and texture features is formed. A region “thesaurus” that contains all the high-level features is constructed using a subtractive clustering method.Then, a “model vector” that contains the distances from each region type is formed and a SVM detector is trained for each semantic concept. Experiments were performed using TRECVID 2005 development data.

/pdf/using-local-region-semantics-for-concept-detection-in-video-2uvbbt1qvn.pdf

Using Local Region Semantics for Concept Detection in Video.

We describe our second-time participation, that includes one high-level feature extraction run, and three manual and one interactive search runs, to the TRECVID video retrieval evaluation. All of these runs have used a system trained on the common development collection. Only visual and textual information were used where visual information consisted of color, texture and edgebased low-level features and textual information consisted of the speech transcript provided in the collection. With the experience gained with our second-time participation, we are in the process of building a system for automatic classification and indexing of video archives.

/pdf/bilkent-university-at-trecvid-2005-2pcsnjh5rx.pdf

Bilkent University at TRECVID 2005

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Bilkent University at TRECVID 2005