Feature Encodings and Poolings for Action and Event Recognition: A Comprehensive Survey <s> VLAD Encoding <s> We address the problem of image search on a very large scale, where three constraints have to be considered jointly: the accuracy of the search, its efficiency, and the memory usage of the representation. We first propose a simple yet efficient way of aggregating local image descriptors into a vector of limited dimension, which can be viewed as a simplification of the Fisher kernel representation. We then show how to jointly optimize the dimension reduction and the indexing algorithm, so that it best preserves the quality of vector comparison. The evaluation shows that our approach significantly outperforms the state of the art: the search accuracy is comparable to the bag-of-features approach for an image representation that fits in 20 bytes. Searching a 10 million image dataset takes about 50ms. <s> BIB001 </s> Feature Encodings and Poolings for Action and Event Recognition: A Comprehensive Survey <s> VLAD Encoding <s> This paper introduces a product quantization-based approach for approximate nearest neighbor search. The idea is to decompose the space into a Cartesian product of low-dimensional subspaces and to quantize each subspace separately. A vector is represented by a short code composed of its subspace quantization indices. The euclidean distance between two vectors can be efficiently estimated from their codes. An asymmetric version increases precision, as it computes the approximate distance between a vector and a code. Experimental results show that our approach searches for nearest neighbors efficiently, in particular in combination with an inverted file system. Results for SIFT and GIST image descriptors show excellent search accuracy, outperforming three state-of-the-art approaches. The scalability of our approach is validated on a data set of two billion vectors. <s> BIB002
For indexation and categorization applications, a high-dimensional BOV representation usually shows better yet robust results. However, the efficiency becomes unbearable when those applications are performed on a large amount of data. Jégou et al. BIB001 proposed a representation, called VLAD (vector of locally aggregated descriptors), for image search on a very large scale. This approach could jointly consider the accuracy, the efficiency, and the computational complexity of the image search. is the set of n local low level d feature vectors extracted from n interest patches of an image, D = {w 1 , w 2 , · · · , w k } ⊂ R d×k be the learned codebook with k-means, and w * = argmin w j ∈D,1≤j≤k x i − w j is the nearest visual word for local patch x i . Then, the VLAD encoding of the image X can be denoted by the accumulation of differences between the visual word w j and its nearest multiple image patches, and 1 ≤ j ≤ k. Jégou et al. BIB001 combined the normalized f VLAD (X) with dimension reduction oriented principal component analysis and indexing oriented approximate nearest neighbor search BIB002 , resulting an accurate, efficient, yet memory friendly image search.