Large-scale target identification method based on mobile platform
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A target recognition and mobile platform technology, applied in the field of image recognition, can solve problems such as no longer applicable image retrieval
Active Publication Date: 2015-03-04
JILIN UNIV
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Problems solved by technology
However, with the development of the network, the use of high-dimensional features in the form of floating-point numbers such as SIFT is no longer suitable for image retrieval on mobile devices.
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Embodiment 1
[0106] Embodiment 1: First, some concepts in the description of the implementation steps are explained.
[0107] 1. Implementation steps of the training process
[0108] 1. First obtain the SIFT descriptor of each image in the image dataset.
[0109] 2. For the set of descriptors of all images in the data set, use the k-means iterative method to minimize the objective function: NS + MD. At the end of this process, we will have binary label information adapted to the distribution properties of the data points in Euclidean space.
[0110] 3. For datasets, divide the datasets into different groups according to their label information. Each group can get a weak hash function.
[0111] 4. Use the AdaBoost mechanism to combine weak hash functions into a strong hash function, further emphasizing that the resulting binary code can maintain the proximity between data points. The resulting binary code can approximately replace the Euclidean distance between data points. An inver...
Embodiment 2
[0126] Datasets: Two popular datasets are used as the retrieved datasets, namely the SIFT1M dataset and the CIFAR10 dataset. Among them, the SIFT1M data set contains a total of 1 million training data sets and 100,000 test data sets. While CIFAR10 contains 60,000 images. 50,000 images are used as the training set and 10,000 images are used as the test set. They all use Top-10as a measure of accuracy.
[0127] Evaluation index: use the general average retrieval accuracy ( mAP ), recall rate and average retrieval time to test the present invention and other best methods in the industry for comparison.
[0128] In the CIFAR10 data set, the feature descriptors of the training data set and the test data set are extracted through the SIFT algorithm. Under the two data sets respectively, use the present invention and the best methods in the industry (KMH[3], ITQ[4], RR[6], LSH[9], SH[5]) to retrieve the test data set respectively in the training nearest neighbors in the datas...
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Abstract
The invention belongs to the field of image recognition and aims to provide a quick and effective large-scale target identification method based on a mobile platform for mobile platform search, and the method can be used for quickly and effectively encoding SIFT (scale invariant feature transform) characteristic points into binary codes with a function of keeping local sensitivity by a Hash algorithm. The method comprises the following steps: obtaining label information of SIFI characteristics X of a database image, wherein the label information consists of '0' and '1'; defining normalized distance similarity and quantization errors; searching a binary label of a data point with minimum value of the sum of NS and MD; obtaining weak Hash functions; combining the weak Hash functions to obtain a strong Hash function. The method is a quick and effective mobile platform search method; the search scheme of mobile equipment can be adjusted according to network conditions, so that responses can be given in time under different network conditions.
Description
technical field [0001] The invention belongs to the field of image recognition. Background technique [0002] In recent years, the nearest neighbor retrieval problem has been widely applied to image retrieval problems, such as image retrieval on mobile devices. However, with the development of the network, the use of high-dimensional features in the form of floating-point numbers such as SIFT is no longer suitable for image retrieval on mobile devices. To solve this problem, people began to use compact binary codes to represent feature descriptors more widely. When the retrieval is performed using binary code, the data structure is simpler and the retrieval speed is faster. [0003] Andoni proposed a locality-sensitive hash algorithm, which is a relatively simple classic algorithm, and the hash function of the algorithm is a randomly generated mapping plane. The hash function has nothing to do with the data. As the number of encoding bits increases, the performance of the...
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