Image Semantic Representation Based Scene Classification Research

How to classify and manage the vast amount of image data using the computer by the way of human understanding becomes an urgent problem in the image under-standing area. Scene analysis and understanding make the image semantic classification possible. The scene classification is clearly identified as a key issue in the image se-mantic classification. This thesis performs the middle-level semantic image represent-tations based on the visual image features, establishes the middle-level semantic concept of image and models it to make up the semantic gap between low-level features and high-level semantics. This thesis achieves the following research results:(1) This thesis proposes a scene classification algorithm based on weighted feature fusion by local entropy. Because the different feature descriptors fit the different scene images, this thesis fuses the two local feature descriptors to strengthen the discri-mination of scene image feature descriptions. Firstly, this thesis analyses the complexity of the scene image by its local entropy quantitatively, and defines the flatness of image. The flatness of each scene category is further to calculated by adding the flatness of each image in this scene category. Secondly, two local feature descriptors are extracted by describing the smooth image and change image, and the image histogram repre-sentation is constructed. Thirdly, the weighted coefficients are obtained by the flatness of scene category. The optimal image representation is obtained by the weighted fusion on the two image histogram representations. Finally, the generative model is trained to perform the scene classification. Experimental results show that this method has some universality on the different image feature descriptions.(2) This thesis presents a scene classification method based on the spatial pyramid image representation by superpixel lattices. Because the traditional image representation method based on BOW (bag-of-words) model ignores the spatial information, this thesis adds it by applying the contextual features and spatial pyramid image representation. Firstly, the multi-scale contextual features are constructed to add the local spatial str-ucture information when performing feature descriptions. Secondly, this thesis applies the superpixel lattices method to segment the image, and the resolutions are determined by the pyramid layers. Thirdly, the image histogram representations of each segmented sub-block region, from superpixel lattices on each level, are formed based on the visual dictionary. These partial sub-representations are weighted to form the whole histogram representation of this image. Finally, the classifier is trained to complete scene image classification. The superpixel lattice based segmentation method avoids the compulsory segmentations of the objects in the image. It ensures the semantic consistency of objects in sub-region. Experimental results demonstrate the superiority of the contextual infor-mation and superpixel lattices segmentation in the scene classification task.(3) This thesis proposes a scene classification method based on feature mapping by locality-constrained linear coding. We extract the visual features of images and generate a visual codebook by clustering, then run feature mapping depending on the visual codebook to form image representation. The feature mapping method, in this thesis, belongs to the way of locality-constrained linear coding based on sum-max pooling. We find out the codewords with the first tth maximum probability and weight them, then take the average weighted values as the feature mapping coding result. This thesis discusses the performance of scene classification related to the value of t and the length of codebook. Experiments prove that the proposed method improves the correlation of codewords and the robustness of feature mapping, and achieves good performance of scene classification.