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Massive image classification method based on deep vector of locally aggregated descriptors (VLAD)

A technology of local features and classification methods, applied in the field of image processing and deep learning, to achieve the effect of improving accuracy

Inactive Publication Date: 2015-03-11
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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  • Application Information

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Problems solved by technology

However, with the sharp increase in the size of image datasets and image diversity, the traditional VLAD gradually shows its inherent disadvantages in massive image classification tasks.
When VLAD is applied to large-scale image sets, the storage of massive high-dimensional VLAD features and subsequent classification operations are extremely challenging for current computer equipment.

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  • Massive image classification method based on deep vector of locally aggregated descriptors (VLAD)
  • Massive image classification method based on deep vector of locally aggregated descriptors (VLAD)
  • Massive image classification method based on deep vector of locally aggregated descriptors (VLAD)

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Embodiment Construction

[0021] The massive image classification method based on deep local feature descriptors proposed by the present invention specifically includes the following steps:

[0022] Step 1: Set the entire deep learning model as the L-layer deep learning process; extract the SIFT feature of each training picture, and the SIFT feature set S of the entire training sample set is expressed as:

[0023] S=[s 1 ,...,S N ], S∈R D×N

[0024] Where N is the number of SIFT features in the SIFT feature set, D is the dimension of each SIFT feature; Kmeans clustering is performed on the SIFT feature set S to obtain the dictionary of the first layer of deep learning process D 1 ∈R D×K , Where K is the dictionary D 1 The number of cluster centers in, D is the dimension of the cluster centers;

[0025] Step 2: Define that each training picture is a set of T=M1×M2×M3 image blocks B=[B 1 ,..., B t ,..., B T ], that is, each training picture has M3 sub-regions, each sub-region is composed of M1×M2 image blocks;...

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Abstract

The invention provides a massive image classification method based on a deep vector of locally aggregated descriptors (VLAD). The method is characterized in that through multi-layered image VLAD extraction, more abstract and richer image expression is finally obtained, so that the purpose of higher image classification accuracy is achieved. The method provided by the invention has the advantages that an adopted deep model can capture more abstract and richer feature information from an image during stepwise feature extraction, and average pooling operation is further conducted, so that captured image features achieve translation invariance, which is critical for the increasing image quantity and the image variety.

Description

Technical field [0001] The invention belongs to the technical field of image processing and deep learning, and relates to high-efficiency mass image processing, in particular to a mass image classification method based on deep local feature descriptors. Background technique [0002] In recent years, image processing has received extensive attention and applications in industry, manufacturing, military, medical and other fields. Although its development situation is very good, as the coverage of practical applications has gradually expanded, massive image data will follow, which makes the traditional image processing methods overwhelmed. The image classification task is mainly composed of three parts: image preprocessing, image feature extraction, and classifier selection. Among them, image feature extraction plays a vital role in image classification tasks. [0003] The local feature descriptors (VLAD: vector of locally aggregated descriptors) proposed by Herve Jegou et al. are c...

Claims

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Application Information

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IPC IPC(8): G06K9/66
CPCG06V10/462G06F18/2411
Inventor 董乐
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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