Deep Learning Image Captioning With Area-Word Explanations
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Solution Overview
Problem
Conventional image captioning technologies struggle to generate complete sentences describing an image and are limited to specific types of images, such as street views, while deep learning algorithms face challenges in interpreting results effectively.
Innovation Solution
An apparatus and method utilizing a deep learning algorithm that includes an image feature extraction module, language generation module, object recognition module, image area-word mapping module, and visualization module to generate and visualize image captions, with a focus on object relevance and Bayesian inference to improve caption generation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional image captioning technologies use nearest-neighbor image matching and label assignment, then the process is simple and fast, but the output is limited to lists of related words rather than complete descriptive sentences
Solution Approach 1:
The patent transforms the output format parameter from simple label lists to complete grammatical sentences by implementing a sentence construction module that assembles subject, predicate, and object components with proper grammatical structure, thereby preserving descriptive information while maintaining generation efficiency
Solution Approach 2:
The patent introduces an intermediary sentence construction module between the nearest-neighbor matching module and the output layer. This intermediary processes the matched labels and synthesizes them into complete descriptive sentences, preventing information loss while maintaining the simplicity of the underlying matching mechanism
2Ease of manufacture
If conventional technologies generate captions using metadata from stored images, then the process is straightforward, but the captions are limited to specific image types such as street views and do not form complete sentences
Solution Approach 1:
The patent implements a universal caption generation framework that can process multiple image types (street views, natural scenes, objects, etc.) through the same pipeline. The system uses generic image processing modules followed by type-specific processing, allowing it to adapt to different image categories while maintaining a unified architecture
Solution Approach 2:
The patent divides the caption generation process into segmented modules: a universal image processing stage, a type-specific feature extraction stage, and a sentence construction stage. This segmentation allows the system to maintain simplicity in the universal parts while adapting to specific image types in the specialized parts
3Loss of information
If deep learning algorithms are used for image caption generation, then the ability to generate complete sentences improves, but the interpretability of results and understanding of the basis for generated captions deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where the system generates multiple candidate captions, evaluates them against the input image using attention mechanisms, and selects the most appropriate caption. This feedback loop provides interpretability by showing which image regions influenced which parts of the generated caption
Solution Approach 2:
The patent introduces attention mechanism modules as intermediaries between the deep learning model and the output captions. These intermediaries generate attention maps that visually display which image regions contributed to each part of the caption, making the black-box deep learning process interpretable
4Measurement precision
If the system maps image areas to words using deep learning, then the accuracy of object-relevant caption generation improves, but the complexity of the system increases
Solution Approach 1:
The patent segments the complex deep learning system into distinct functional modules: image preprocessing module, feature extraction module, area-word mapping module, and sentence construction module. Each module has a specific function, making the overall complex system manageable and interpretable while maintaining high accuracy
Data Source
AI summary
An apparatus and method for automatically generating an image caption is provided capable of giving an explanation by using Bayesian inference and an image area-word mapping module on the basis of a deep learning algorithm. An apparatus for automatically generating an image caption, according to one embodiment of the present invention, includes: an automatic caption generation module for creating a caption by applying a deep learning algorithm to an image received from a client; a caption basis generation module for creating a basis for the caption by mapping a partial area in the image received from the client with respect to important words in the caption received from the automatic caption generation module; and a visualization module for visualizing the caption received from the automatic caption generation module and the basis for the caption received from the caption basis generation module to return same to the client.