Image Retrieval Oriented Massive Images Automatic Clustering Methods Research

With the development of mobile Internet and multimedia technologies, it makes im-ages data being explosive growth. Faced with massive image data, how to manage and retrieve image library conveniently and effectively, and discover valuable information hidden in the image library are problems that need to be solved. With the help of clustering techniques in data mining and content-based image retrieval technology, bring opportunities to solve this problem. As a non-supervised learning method cluster-ing analysis can achieve classification with unlabeled samples. The content-based re-trieval gets rid of original text search, is more objective and convenient than annota-tion-based retrieval.To solve this problem, this paper attempts to propose a clustering method to com-plete initial management of massive image library. Through clustering over image data-base, it can achieve image category and annotation. And use content-based retrieval technology to retrieval image library. With thoughts described above, this paper builds an image database clustering and retrieval platform. And to overcome the insufficient of clustering algorithms and traditional image features, proposes improved feature extrac-tion algorithms and clustering algorithms. The main research work and innovative con-tent are described as the following content:(1) Propose an improved method against shortcomings of traditional image features. Proposed block color histogram with spatial information and block LBP fea-tures; Color-SIFT features proposed by combining color information, to make up shortcomings that SIFT features use only gray information; Proposed joint features which integrated Dense SIFT features and color information, it can describe the local area while preserving the global information of the image, experimental results show that its performance is better than other features.(2) Mini-Batch K-means is proposed based on K-means algorithm. It not only im-proves the stability of the algorithm, while greatly enhances the speed of the algorithm. The kernel function is introduced to spectral clustering algorithm and thus proposes spectral clustering based on Gaussian kernel. It can enhance the performance of spectral clustering algorithm.(3) Summarized and analyzed the relevant image features, including global features and local features. Color features are color histogram, color moment, etc. Tex-ture feature are LBP, GLCM etc. Shape features are Hu moments, edge histo- gram etc. Basically explained aspects of the local features of the classic fea-tures SIFT and SURF.(4) Research and study the relevant papers, reviews the classical clustering algo-rithms and the latest clustering algorithms. Explain these principles and cluster-ing step of clustering algorithm in detail. Summarize the advantages and disad-vantages of the algorithm.(5)Design and implements image retrieval clustering platform. Collate and analyze the image features and clustering algorithms. Integrated those algorithms into the platform. At the same time based on the results of cluster analysis improves retrieval speed.