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Mass image retrieval system based on cluster compactness

An image retrieval and clustering technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve a large number of computing and storage costs

Active Publication Date: 2014-02-26
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

However, the method based on the vocabulary tree needs to encode thousands of features in the image to generate a vocabulary bag according to the visual vocabulary, which still leads to a large amount of computational and storage overhead.

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  • Mass image retrieval system based on cluster compactness
  • Mass image retrieval system based on cluster compactness
  • Mass image retrieval system based on cluster compactness

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Embodiment Construction

[0073] In order to make the objectives, technical solutions, and beneficial effects of the present invention clearer, the following describes the present invention in further detail in combination with specific cases and with reference to the accompanying drawings.

[0074] The invention is used in a massive image library, especially a fast search method for similar images containing one million or more image libraries. This method can obtain the compact feature of the cluster by calculating the local visual features extracted from the image through clustering and the corresponding local feature distribution histogram and spatial distribution information. At the same time, it applies the index structure based on the improved vocabulary tree and uses the compact feature for retrieval. , Can efficiently complete the retrieval work of massive images. The retrieval method can well meet the needs of users for fast and effective retrieval methods, and at the same time, it can greatly i...

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Abstract

The invention belongs to the technical field of mode recognition and information processing and provides a mass image retrieval system based on cluster compactness. Steps include 1, calculating local features of images in a sample image library and a test image library; 2, calculating cluster compactness of each image, namely clustering the local features to acquire each type of cluster centers, counting a local feature distribution histogram and spatial statistical information of each cluster, and generating cluster compactness; 3, randomly sampling cluster compactness of the sample image library, clustering components of the cluster centers in the sampled cluster compactness to generate a vocabulary tree, and quantizing the cluster compactness of the images in the test image library to the vocabulary tree to generate corresponding inverted files; 4, retrieving by a modified retrieval algorithm based on the vocabulary tree, namely retrieving, by retrieving the inverted files in the vocabulary tree and calculating the weight of similarity between retrieval images and the image library image cluster compactness.

Description

Technical field [0001] The present invention belongs to the technical field of pattern recognition and information processing, and relates to mass image processing in computer vision, and in particular to a research and implementation scheme of mass image retrieval based on clustering compact features. This solution uses a cluster-based compact description of visual features and an improved vocabulary tree-based retrieval algorithm to quickly and efficiently retrieve similar pictures from massive images. Background technique [0002] At present, with the explosive growth of Internet image data, how to retrieve the most similar pictures from image big data has become a very challenging topic and has attracted a lot of research work. Image retrieval has a wide range of application scenarios, and its application value is getting higher and higher in e-commerce, social networking, product or scenery recommendation, etc. Consider one of the most extensive and common scenarios. The us...

Claims

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/5838
Inventor 董乐梁燕封宁谢山山
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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