Abstract:

Multimedia information, especially videos, is growing explosively with the rapid development of the Internet and multimedia technology. Due to its variety of image features, it is capable of reaching several hundred dimensions and even thousands of dimensions. Storing and indexing the high-dimensional feature vectors has become key technologies of content-based video retrieval. The residual quantization mechanism, which combines the asymmetric distance and set sorting algorithm based on multi-feature candidates, is improved after analyzing the characteristics of soccer videos. For soccer videos, SD-VLAD (Soft Distribution-Vectors of Locally Aggregated Descriptors), BOC (Bag of Color), and shot type are selected for describing the information of images. To address the problem that the original residual quantized inverted index can only retrieve single features, multiple feature retrieval and sorting are proposed. In the stage of candidate set sorting, a multi-feature based similarity calculation method is designed according to the shots type. The experimental results show that multi-feature hierarchical retrieval and sorting can be achieved at the cost of memory space. While ensuring query speed, the accuracy of the query is improved.