Improved Codebook Model Research Articles

A deep learning algorithm for fast motion video sequences based on improved codebook model

A deep learning algorithm for fast motion video sequences based on improved codebook model

Sparse Codebook Model of Local Structures for Retrieval of Focal Liver Lesions Using Multiphase Medical Images.

Characterization and individual trait analysis of the focal liver lesions (FLL) is a challenging task in medical image processing and clinical site. The character analysis of a unconfirmed FLL case would be expected to benefit greatly from the accumulated FLL cases with experts' analysis, which can be achieved by content-based medical image retrieval (CBMIR). CBMIR mainly includes discriminated feature extraction and similarity calculation procedures. Bag-of-Visual-Words (BoVW) (codebook-based model) has been proven to be effective for different classification and retrieval tasks. This study investigates an improved codebook model for the fined-grained medical image representation with the following three advantages: (1) instead of SIFT, we exploit the local patch (structure) as the local descriptor, which can retain all detailed information and is more suitable for the fine-grained medical image applications; (2) in order to more accurately approximate any local descriptor in coding procedure, the sparse coding method, instead of K-means algorithm, is employed for codebook learning and coded vector calculation; (3) we evaluate retrieval performance of focal liver lesions (FLL) using multiphase computed tomography (CT) scans, in which the proposed codebook model is separately learned for each phase. The effectiveness of the proposed method is confirmed by our experiments on FLL retrieval.

Dynamic foreground detection based on improved Codebook model

Foreground detection method based on improved Codebook algorithm is discussed in this paper: first of all, transform RGB colour space into YCbCr colour space to make chromaticity convergent, and cope with illumination changing. Second, search for the CodeWords matching with the pixel values of the YCbCr to update the algorithm, so as to ensure the foreground detection effect. Then apply random abandon value method to delete the CodeWords that have not been accessed for a long time, reduce memory consumption and improve processing speed. Finally, conduct experiments on the infrared imagery and the colour image, respectively, to make foreground detection. The results demonstrate that the model and algorithm presented in this paper can make better foreground detection and reduce memory consumption. Meanwhile, compare this method with other algorithms to prove its advantages.

改进的基于码本模型目标检测算法

改进的基于码本模型目标检测算法