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Improving the Fisher Kernel for Large-Scale Image Classification
TLDR
In an evaluation involving hundreds of thousands of training images, it is shown that classifiers learned on Flickr groups perform surprisingly well and that they can complement classifier learned on more carefully annotated datasets.
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Iterative Quantization: A Procrustean Approach to Learning Binary Codes for Large-Scale Image Retrieval
TLDR
This paper addresses the problem of learning similarity-preserving binary codes for efficient similarity search in large-scale image collections by proposing a simple and efficient alternating minimization algorithm, dubbed iterative quantization (ITQ), and demonstrating an application of ITQ to learning binary attributes or "classemes" on the ImageNet data set.
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Aggregating Local Image Descriptors into Compact Codes
TLDR
This paper first presents and evaluates different ways of aggregating local image descriptors into a vector and shows that the Fisher kernel achieves better performance than the reference bag-of-visual words approach for any given vector dimension.
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Image Classification with the Fisher Vector: Theory and Practice
TLDR
This work proposes to use the Fisher Kernel framework as an alternative patch encoding strategy: it describes patches by their deviation from an “universal” generative Gaussian mixture model, and reports experimental results showing that the FV framework is a state-of-the-art patch encoding technique.
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Fisher Kernels on Visual Vocabularies for Image Categorization
TLDR
This work shows that Fisher kernels can actually be understood as an extension of the popular bag-of-visterms, and proposes 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 in images.
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AVA: A large-scale database for aesthetic visual analysis
TLDR
A new large-scale database for conducting Aesthetic Visual Analysis: AVA, which contains over 250,000 images along with a rich variety of meta-data including a large number of aesthetic scores for each image, semantic labels for over 60 categories as well as labels related to photographic style is introduced.
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Large-scale image retrieval with compressed Fisher vectors
TLDR
This article shows why the Fisher representation is well-suited to the retrieval problem: it describes an image by what makes it different from other images, and why it should be compressed to reduce their memory footprint and speed-up the retrieval.
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Label-Embedding for Attribute-Based Classification
TLDR
This work proposes to view attribute-based image classification as a label-embedding problem: each class is embedded in the space of attribute vectors, and introduces a function which measures the compatibility between an image and a label embedding.
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Adapted Vocabularies for Generic Visual Categorization
TLDR
A novel and practical approach to GVC is proposed, 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.
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Label-Embedding for Image Classification
TLDR
This work proposes to view attribute-based image classification as a label-embedding problem: each class is embedded in the space of attribute vectors, and introduces a function that measures the compatibility between an image and a label embedding.
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