Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Distortion Invariant Object Recognition in the Dynamic Link Architecture
IEEE Transactions on Computers
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Relation between PLSA and NMF and implications
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Constructing visual phrases for effective and efficient object-based image retrieval
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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Having effective methods to access the images with desired object is essential nowadays with the availability of huge amount of digital images. We propose a semantic higher-level visual representation which improves the traditional part-based bag-of words image representation, in two aspects. First, we propose a semantic model to generate a semantic visual words and phrases in order to bridge the semantic gab factor. Second, the approach strengthens the discrimination power of classical visual words by constructing an mid level descriptor, Semantic Visual Phrase, from frequently co-occurring Semantic Visual Words set in the same local context.