Dialog-Based Image Retrieval Using Multimodal Contextual Data
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Solution Overview
Problem
Current image retrieval systems face challenges in effectively understanding user intent due to a semantic gap between visual features and high-level semantic concepts, and they are limited by relying on restricted user interaction modalities, such as natural language, which constrains the information that can be conveyed for improved retrieval.
Innovation Solution
The implementation of a method that utilizes multimodal contextual information to enhance dialog-based interactive image retrieval by combining natural language feedback with visual attributes, allowing for the denoising and completion of contextual information to improve user feedback modeling and retrieval performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional relevance feedback or relative attribute feedback is used, then the system can process user input, but the retrieval performance is limited due to the semantic gap between visual features and high-level semantic concepts
Solution Approach 1:
The patent introduces side information (metadata, contextual data) as an intermediary element that bridges the semantic gap between visual features and user intent. This side information serves as a mediator that enriches the representation of both images and user queries, enabling better alignment without requiring direct complex visual-semantic mapping
Solution Approach 2:
The patent combines multiple types of information (visual features, natural language feedback, and side information) to create a composite representation. This composite approach integrates heterogeneous data sources to form a more complete and accurate model of user intent, improving retrieval performance beyond what any single modality could achieve
2Ease of operation
If natural language feedback is used for user interaction, then the system is easy to operate, but the information that can be conveyed is constrained
Solution Approach 1:
The patent merges natural language feedback with side information from multiple modalities (visual attributes, metadata, contextual data). This combination allows the system to maintain the ease of natural language interaction while enriching the information available for retrieval, as the side information compensates for the limitations of text-based input
3Measurement precision
If side information is integrated into the retrieval process, then retrieval performance improves, but the system complexity increases
Solution Approach 1:
The patent performs preliminary processing of side information during the indexing phase, extracting and organizing metadata and contextual data before retrieval occurs. This advance preparation reduces the computational burden during actual query processing, as the heavy lifting of information extraction and organization is completed beforehand
Solution Approach 2:
The system automatically extracts and processes side information from various sources without requiring manual intervention. The retrieval model self-adapts to incorporate relevant side information based on the query and image characteristics, reducing the need for complex manual configuration and management
Data Source
AI summary
A method includes receiving input from a client at least partially specifying one or more characteristics, wherein the initial input includes a seed image and a natural language statement describing a desired change to the seed image; predicting one or more attributes of the seed image by operation of a neural network on the seed image; and parsing the natural language statement to identify desired changes to the one or more attributes of the seed image. The method also includes generating an interim target image by changing the one or more attributes of the seed image, according to the parsed natural language statement; selecting a set of images from an image database for output to the client, each of said set of images being determined to at least partially satisfy the one or more changed attributes of the seed image; and displaying the set of images to the client.