Scene categorization based on integrated feature description and local weighted feature mapping

Local and global features are considerably important features in computer vision and play an important role in scene categorization task. In this paper, an integrated feature description for scene categorization is constructed. First, we extract a type of extended contextual features for scene images that contain the local gradient information and more comprehensive local structural information. Mapping the local features by using improved LLC (Local-constrained Linear Coding) scheme to form the original image representation; Secondly, a set of global features named ‘gist’ are extracted that provide a statistical summary of the spatial layout properties of the scene; Then, the contextual features and ‘gist’ features are weighted combined based on their contribution for the integrated feature description, and each image is represented by using LLC scheme. Finally, we perform the scene categorization by libSVM with the HIK (Histogram Intersection Kernel) function. The proposed method achieves a satisfactory average accuracy rate 87.60% on a set of 15-scene categories.

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► Extended contextual features with richer local structural information are extracted.
► We extract global features ‘gist’ that provides spatial layout properties of scene.
► We perform original image representation by using the improved LLC scheme.
► The original image representation and ‘gist’ features are weighted combined.
► Finally, we train LIBSVM with HIK for the scene recognition.