Indoor scene semantic annotation method based on RGB-D data

Abstract

The invention relates to an indoor scene semantic annotation method based on RGB-D data. According to the method, a coarse-to-fine global recursion feedback semantic annotation framework based on the RGB-D data is built, in addition, the whole semantic annotation framework is divided into two major parts including the coarse-granularity region stage semantic label deduction and fine-granularity pixel stage semantic label refinement. The framework is different from the traditional region stage or pixel stage semantic annotation framework, the framework rebuilds the relationship between the coarse-granularity region stage semantic label deduction and the fine-granularity pixel stage semantic annotation, and a reasonable global recursion feedback mechanism is introduced, so that the coarse-granularity region stage semantic annotation result and the fine-granularity pixel level semantic annotation result realize the alternate iterative updating optimization. Through adopting the mode, the multi-mode information of different region layers in the scene images is better merged, and the general problem that an annotation base element is difficult to be properly selected in the traditional indoor scene semantic annotation scheme is solved to a certain degree.

Description

technical field [0001] The invention relates to an image semantic labeling method, in particular to an indoor scene semantic labeling method based on RGB-D data, and belongs to the technical field of semantic label classification of computer vision. Background technique [0002] Image semantic annotation is the core unit of scene understanding work in computer vision, and its basic goal is to densely provide a predefined semantic category label for each pixel in a given query image. Considering the ambiguity, complexity and abstraction of image semantics, the image semantic models generally established are hierarchical. Among them, "target semantics" is at the middle level of the semantic hierarchy, and plays a linking role in many high-level semantic reasoning processes. According to the quantitative level of annotation primitives in image semantic annotation, most current image semantic annotation schemes can be roughly divided into two categories, including: pixel-level ...

AI Technical Summary

Problems solved by technology

However, most current semantic annotation works only use depth information to construct region-level features, but ignore its role in context inference, and the depth information used is relatively simple.

[0007] To sum up, the existing semantic annotation schemes for indoor scenes generally have the problem that it is difficult to select the quantization level of the annotation primitives, and the role of geometric depth information in the context reasoning process has not received enough attention.

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