Binary image feature similarity discrimination method based on random forest algorithm

A random forest algorithm and binary image technology, applied in computing, computer parts, character and pattern recognition, etc., can solve the problem of no exact matching, achieve the effect of improving retrieval speed, improving average retrieval accuracy, and improving matching speed

Active Publication Date: 2016-07-13
XI AN JIAOTONG UNIV
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Most of the current research is only carried out to the setting matching of the thresh...

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  • Binary image feature similarity discrimination method based on random forest algorithm
  • Binary image feature similarity discrimination method based on random forest algorithm
  • Binary image feature similarity discrimination method based on random forest algorithm

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Embodiment Construction

[0028] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0029] In the offline indexing stage, the features in the image library are extracted, and the feature library is established; in the online retrieval stage, the features of the query image are extracted, matched with the features in the feature library, and the matched features are input into the random forest discriminant model. Voting mechanism, output retrieval results.

[0030] see figure 1 , the present invention is based on random forest algorithm to distinguish binary image characteristic similar realization method, comprises the following steps:

[0031] 1) In the offline indexing stage, the scale-invariant feature conversion feature of the image is extracted, each dimension of all features is regarded as a vector and clustered with the K-means method to obtain 5 cluster centers, and then the scale-invariant feature conversion feature...

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Abstract

The invention discloses a binary image feature similarity discrimination method based on a random forest algorithm, comprising an off-line index phase: extracting the scale invariant feature transform conversion features of an image, employing each dimension of all features as a vector to be used for clustering through a K mean value method, and quantifying scale invariant feature transform conversion features into 512 dimension binary system features; and writing quantified features, feature indexing, image names corresponding to features and adjacent features into a database to be used as an image feature database. In the off-line index phase, the scale invariant feature transform conversion features of the image are extracted, and are quantified to be 512 dimension binary system features to match features in the image feature database, and find adjacent features; and then a random forest algorithm is employed to discriminate the adjacent features, and a voting mechanism retrieves similar images.

Description

Technical field: [0001] The invention relates to similar image retrieval in the technical field of image processing, in particular to a method for realizing the similarity of distinguishing binary image features based on a random forest algorithm. Background technique: [0002] With the rapid development of Internet technologies such as big data and cloud computing, image files and related materials stored on the Internet have increased rapidly. At present, there are hundreds of millions of Internet images. How to store these large-scale Quickly and accurately retrieving the pictures that users want from the image database has become an important research direction in the field of computer vision. [0003] Traditional image retrieval models such as Bag of Words (BoW) and Local Aggregation Vectors (VLAD), when indexing images offline, first cluster the features of the image, the cluster centers are used as visual words, and then the features are quantized into visual words. ...

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Application Information

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IPC IPC(8): G06K9/46G06K9/62G06K9/66G06F17/30
CPCG06F16/583G06V10/462G06V30/194G06F18/23213
Inventor 王霞王珊马涛
Owner XI AN JIAOTONG UNIV
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