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» Estimating Missing Features to Improve Multimedia Retrieval
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ICMCS
2005
IEEE
153views Multimedia» more  ICMCS 2005»
15 years 5 months ago
Improved semantic region labeling based on scene context
Semantic region labeling in outdoor scenes, e.g., identifying sky, grass, foliage, water, and snow, facilitates content-based image retrieval, organization, and enhancement. A maj...
Matthew R. Boutell, Jiebo Luo, Christopher M. Brow...
ICMCS
2006
IEEE
140views Multimedia» more  ICMCS 2006»
15 years 5 months ago
Evolutionary Feature Generation in Speech Emotion Recognition
Feature sets are broadly discussed within speech emotion recognition by acoustic analysis. While popular filter and wrapper based search help to retrieve relevant ones, we feel th...
Björn Schuller, Stephan Reiter, Gerhard Rigol...
WWW
2007
ACM
16 years 11 days ago
Predicting clicks: estimating the click-through rate for new ads
Search engine advertising has become a significant element of the Web browsing experience. Choosing the right ads for the query and the order in which they are displayed greatly a...
Matthew Richardson, Ewa Dominowska, Robert Ragno
MM
2005
ACM
171views Multimedia» more  MM 2005»
15 years 5 months ago
Semantic manifold learning for image retrieval
Learning the user’s semantics for CBIR involves two different sources of information: the similarity relations entailed by the content-based features, and the relevance relatio...
Yen-Yu Lin, Tyng-Luh Liu, Hwann-Tzong Chen
PCM
2001
Springer
145views Multimedia» more  PCM 2001»
15 years 4 months ago
Color Content Matching of MPEG-4 Video Objects
Abstract. Color histogram is one of the most widely used visual feature representations in content-based retrieval. When processing the image/video data in the JPEG/MPEG compressed...
Berna Erol, Faouzi Kossentini