Request PDF on ResearchGate | Hierarchical Gaussianization for Image Classification | In this paper, we propose a new image representation to capture both. In this paper, we propose a new image representation to capture both the appearance and spatial information for image classification. Hierarchical Gaussianization for Image Classification. Xi Zhou.. cal Gaussianization, each image is represented by a Gaus-. please see the pdf file.

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Cited Source Add To Collection. Skip to search form Skip to main content. Semantic image representation for visual recognition. Showing of 30 references. Are you looking for Bernt Schiele 77 Estimated H-index: Learning hybrid part filters for scene recognition.

Caltech object category dataset. Disruption-tolerant networking protocols and services for disaster response communication. In this paper, we propose a new image representation to capture both the appearance and spatial information for image classification applications. Computer vision Mixture model Dimensionality reduction.


Hierarchical Gaussianization for image classification – Semantic Scholar

Showing of extracted citations. VeenmanArnold W. Florent Perronnin 43 Estimated H-index: Efficient highly over-complete sparse coding using a mixture model. A k-means clustering algorithm.

Hatch 4 Estimated H-index: Shrinkage Expansion Adaptive Metric Learning. A practical view of large-scale classification: Caltech Object Category Dataset.

Hierarchical Gaussianization for image classification

Beyond Bags of Features: Learning representative and discriminative image representation by deep appearance and spatial coding. Classivication University of British Columbia. A GMM parts based face representation for improved verification through relevance adaptation. Topics Discussed in This Paper. Facial recognition system Computer vision Mathematics Histogram Mixture model Gaussian process Dimensionality reduction Contextual image classification Feature vector Machine learning Artificial intelligence Spatial analysis Pattern recognition.

We justify that the traditional histogram representation and the spatial pyramid matching are special cases of our hierarchical Gaussianization. Simon Lucey 31 Estimated H-index: We compare our new representation with other approaches in scene classification, object recognition and face recognition, and our performance ranks among the top in all three tasks. Hanlin Goh 7 Classificatikn H-index: Adapted vocabularies for generic visual categorization.


From This Paper Figures, tables, and topics from this paper. See our FAQ for additional information. Hieerarchical 58 Estimated H-index: Gregory Griffin 2 Estimated H-index: Probabilistic Elastic Part Model: After such a hierarchical Gaussianization, each image is represented by a Gaussian mixture model GMM for its appearance, and several Gaussian maps for its spatial layout.

Bingyuan Liu 4 Estimated H-index: Spatially local coding for object recognition.

Farquhar 1 Estimated H-index: Kuhl Rochester Institute of Technology. Real-world acoustic event detection pattern recognition letters [IF: Cited 40 Source Add To Collection.