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Improving the Fisher Kernel for Large-Scale Image Classification
TLDR
The Fisher kernel (FK) is a generic framework which combines the benefits of generative and discriminative approaches. Expand
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Iterative Quantization: A Procrustean Approach to Learning Binary Codes for Large-Scale Image Retrieval
TLDR
We formulate the problem of learning a good binary code in terms of directly minimizing the quantization error of mapping this data to the vertices of a zero-centered binary hypercube and propose a simple and efficient alternating minimization algorithm to accomplish this task. Expand
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  • 346
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Aggregating Local Image Descriptors into Compact Codes
TLDR
We present and evaluate different ways of aggregating local image descriptors into a vector and show that the Fisher kernel achieves better performance than the reference bag-of-visual words approach for any given vector dimension. Expand
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Image Classification with the Fisher Vector: Theory and Practice
TLDR
A standard approach to describe an image for classification and retrieval purposes is to extract a set of local patch descriptors, encode them into a high dimensional vector and pool them into an image-level signature. Expand
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Fisher Kernels on Visual Vocabularies for Image Categorization
  • F. Perronnin, C. Dance
  • Mathematics, Computer Science
  • IEEE Conference on Computer Vision and Pattern…
  • 17 June 2007
TLDR
We propose to apply this framework to image categorization where the input signals are images and where the underlying generative model is a visual vocabulary: a Gaussian mixture model which approximates the distribution of low-level features. Expand
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AVA: A large-scale database for aesthetic visual analysis
TLDR
We introduce a novel large-scale database called AVA (Aesthetic Visual Analysis), which contains more than 250,000 images, along with a rich variety of annotations. Expand
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Large-scale image retrieval with compressed Fisher vectors
TLDR
The problem of large-scale image search has been traditionally addressed with the bag-of-visual-words (BOV). Expand
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Label-Embedding for Attribute-Based Classification
TLDR
We propose to view attribute-based image classification as a label-embedding problem: each class is embedded in the space of attribute vectors. Expand
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Adapted Vocabularies for Generic Visual Categorization
TLDR
We propose a novel and practical approach to GVC based on a universal vocabulary, which describes the content of all the considered classes of images, and class vocabularies obtained through the adaptation of the universal vocabulary using class-specific data. Expand
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Label-Embedding for Image Classification
TLDR
We propose to view attribute-based image classification as a label-embedding problem: each class is embedded in the space of attribute vectors. Expand
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