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Image retrieval method based on weight color-sift feature dictionary

A feature dictionary and image retrieval technology, which is applied in electronic digital data processing, special data processing applications, instruments, etc., can solve the problems of increasing computational complexity, complex similarity measurement, returning retrieval result set callback rate and retrieval rate, etc. Achieve the effect of improving speed and accuracy, multiplicity and efficiency, improving accuracy and recall rate

Active Publication Date: 2017-02-08
XIDIAN UNIV
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  • Claims
  • Application Information

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Problems solved by technology

Although this method combines the interactive image retrieval method of user evaluation and annotation to obtain high-level semantic information and improve the accuracy of real-time retrieval, when it is applied to a large image database, due to the complicated similarity metrics of various images, the processing is complicated. The labeling increases the computational complexity and reduces the retrieval efficiency of the image, resulting in low callback rate and retrieval rate of the returned retrieval result set

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  • Image retrieval method based on weight color-sift feature dictionary

Examples

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Embodiment 1

[0029] The implementation of the image retrieval method based on the weight color-sift feature dictionary of the present invention refers to figure 1 , give the following specific examples:

[0030] Step 1: Randomly select one image for each type of image in the image database to be retrieved to form the training image database. This example uses the Corel-1000 image database, and it is necessary to retrieve the same type of image in the Corel-1000 image database. The image database includes There are 10 categories of images, and each category includes 100 images. In this example, the value of l is 10, a total of 10 categories, and a total of 100 training images are selected.

[0031] Step 2: Randomly select a training image in the training image database for grayscale transformation, process it through a direction-tunable filter, select a two-dimensional Gaussian function as the filter kernel function, and select an appropriate filter sliding window size to obtain each The e...

Embodiment 2

[0046] Embodiment 2 The image retrieval method based on the weight color-sift feature dictionary is the same as that in Embodiment 1

[0047] This example also selects the Corel-1000 image database. The image database includes 10 categories of images, and each category includes 100 images. The same retrieval process as in Embodiment 1 is performed for each image in the database, and the number of retrieved images n is calculated when returning For the average retrieval accuracy rate of each of the 10 categories at 20 o'clock and the average retrieval accuracy rate of 1000 images in all 10 categories, the retrieval results are counted and tabulated, and compared with several well-known retrieval methods in the prior art in the art For example, the method proposed by Jhanwar and Hung was compared with the method based on color-texture-shape, the method based on SIFT-BOF and the method based on SIFT-SPM. The comparison results are shown in Table 1. As can be seen from Table 1, th...

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Abstract

The invention discloses an image retrieval method based on a weight color-sift characteristic dictionary. The image retrieval method based on the weight color-sift characteristic dictionary comprises the following steps that training images are selected in images to be retrieved randomly, the edges of the training images are extracted, the color-sift characteristics of edge points of all the training images are extracted, and a characteristic dictionary is constructed according to the color-sift characteristics, an image which needs to be retrieved is input and the color-sift characteristics of the retrieval image and the edge points of the image to be retrieved are extracted, and weight histogram characteristics of the retrieval image and the image to be retrieved are extracted based on the characteristic dictionary; similarity matching based on the weight histogram characteristics is conducted on the retrieval image and the images to be retrieved in the database based on the weight histogram characteristics; whether all the images to be retrieved in the database are all traversed is detected,, the result is matched according to similarity and the image searching result is displayed if all the images to be retrieved in the database are all traversed, and the similarity matching is conducted again if all the images to be retrieved in the database are not traversed. The image retrieval method based on the weight color-sift characteristic dictionary improves accuracy and callback rate during searching of a large-scaled image database, has dimension invariance, translation invariance and rotation invariance, and has the advantages that the image retrieval method based on the color-sift characteristic dictionary has locality, particularity, multi-amount property and high-efficiency property.

Description

technical field [0001] The invention belongs to the technical field of image retrieval, and specifically relates to an image retrieval method based on a weight color-sift feature dictionary, which is based on image content and image feature extraction to realize the process of image analysis and retrieval, and allows users to input a or multiple images to find other images with the same or similar content. Background technique [0002] An image is a similar and vivid description or portrait of an objective object. In other words, an image is a representation of an objective object, which contains information about the object being described. It is people's primary source of information. According to statistics, about 75% of the information a person obtains comes from vision. As the saying goes, "seeing is better than hearing a hundred times" and "clear at a glance", both reflect the unique effect of images in information transmission. How to quickly and accurately extrac...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F17/30
Inventor 李平舟刘燕刘宪龙杨国瑞孙雪萍赵楠
Owner XIDIAN UNIV
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