Similarity learning via dissimilarity space in CBIR
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Benchmarking multimedia search in structured collections
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
The challenge problem for automated detection of 101 semantic concepts in multimedia
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
The influence of cross-validation on video classification performance
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Multimedia retrieval at INEX 2006
ACM SIGIR Forum
Semi-supervised learning for semantic video retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Proceedings of the international workshop on Workshop on multimedia information retrieval
Adapting appearance models of semantic concepts to particular videos via transductive learning
Proceedings of the international workshop on Workshop on multimedia information retrieval
Query on demand video browsing
Proceedings of the 15th international conference on Multimedia
Multimedia retrieval at INEX 2007
ACM SIGIR Forum
The INEX 2007 Multimedia Track
Focused Access to XML Documents
Semantic Scene Classification for Image Annotation and Retrieval
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
SemanGist: A Local Semantic Image Representation
PCM '08 Proceedings of the 9th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Foundations and Trends in Information Retrieval
Image categorization combining neighborhood methods and boosting
LS-MMRM '09 Proceedings of the First ACM workshop on Large-scale multimedia retrieval and mining
Image annotation using clickthrough data
Proceedings of the ACM International Conference on Image and Video Retrieval
The Pascal Visual Object Classes (VOC) Challenge
International Journal of Computer Vision
Comparing compact codebooks for visual categorization
Computer Vision and Image Understanding
Learning natural scene categories by selective multi-scale feature extraction
Image and Vision Computing
Overview of the WikipediaMM task at ImageCLEF 2008
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
Overview of the wikipediaMM task at ImageCLEF 2009
CLEF'09 Proceedings of the 10th international conference on Cross-language evaluation forum: multimedia experiments
An eye fixation database for saliency detection in images
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Region Contextual Visual Words for scene categorization
Expert Systems with Applications: An International Journal
Semantics extraction from images
Knowledge-driven multimedia information extraction and ontology evolution
Reliability and effectiveness of clickthrough data for automatic image annotation
Multimedia Tools and Applications
Bag of spatio-visual words for context inference in scene classification
Pattern Recognition
Hamming selection pruned sets (HSPS) for efficient multi-label video classification
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
Topic modelling of clickthrough data in image search
Multimedia Tools and Applications
Hi-index | 0.00 |
We present a generic and robust approach for scene categorization. A complex scene is described by proto-concepts like vegetation, water, fire, sky etc. These proto-concepts are represented by low level features, where we use natural images statistics to compactly represent color invariant texture information by a Weibull distribution. We introduce the notion of contextures which preserve the context of textures in a visual scene with an occurrence histogram (context) of similarities to proto-concept descriptors (texture). In contrast to a codebook approach, we use the similarity to all vocabulary elements to generalize beyond the code words. Visual descriptors are attained by combining different types of contexts with different texture parameters. The visual scene descriptors are generalized to visual categories by training a support vector machine. We evaluate our approach on 3 different datasets: 1) 50 categories for the TRECVID video dataset; 2) the Caltech 101-object images; 3) 89 categories being the intersection of the Corel photo stock with the Art Explosion photo stock. Results show that our approach is robust over different datasets, while maintaining competitive performance.