Home Engineering Cellular Image Classification
The Algorithm Framework
Our proposed LLDC method utilizes local distance vector to generate discriminative and effective image features, then adopts coding-pooling framework to obtain robust image representation. To evaluate the effectiveness and generalization of the proposed local distance transformation, we apply two different linear coding method respectively, i.e., LLC  and LSC , to encode local distance vectors due to their high efficiency and prominent performance.
Within the proposed LLDC framework, the local distance vectors are firstly transformed from local features. Then the local distance vectors and the original local features are separately encoded and pooled to generated two image representations. Finally, we directly concatenate them to extract more discriminative and descriptive image representation while they are complementary to each other.
The overview of the LLDC framework is shown in Fig. 5.2 including following steps:
Fig. 5.2 Overview of the image classification flowchart based on our proposed LLDC method
(4) Max-pooling is performed on the codes within each spatial subregion Ii as follow:
where max is performed element-wisely for the involved vectors in each subregion and i = 1, 2,..., L is the numbering of subregions;
(5) The image representation based on local distance vector can be generated by concentrating all the pooled features from every subregion, i.e., V = V1; V2’ ...’ V'f ]. And the representation is normalized by
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