Hierarchical Classifiers for Image Text Search Indexing
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Solution Overview
Problem
Current search systems rely on limited indexed information, leading to inaccurate search results due to the lack of relevant descriptive terms, as illustrated by the example of a search for 'red desk' returning non-red results.
Innovation Solution
The integration of image recognition and hierarchical classifiers to generate additional tags from both image and text data, expanding the indexed information and improving search accuracy by analyzing product images and utilizing text data to guide image recognition, thereby enhancing the search index with descriptive attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If search indexes store only limited preprocessed information, then the search system operates efficiently with low storage requirements, but the search accuracy deteriorates due to insufficient descriptive terms
Solution Approach 1:
The system performs preliminary image recognition and tag generation during the indexing phase, extracting descriptive attributes (colors, patterns, materials) from product images before search queries are submitted. This preliminary action enriches the search index with visual descriptors that improve search accuracy without requiring complex processing during actual search operations
Solution Approach 2:
The patent introduces an intermediary image recognition system that processes product images and generates descriptive tags, acting as a mediator between the product database and the search index. This intermediary extracts visual features (colors, patterns, materials) and transforms them into searchable descriptors, enabling accurate visual-based search without storing entire images in the index
2Measurement precision
If comprehensive image and text data are indexed, then search accuracy improves by capturing more descriptive attributes, but the system complexity increases due to additional processing requirements
Solution Approach 1:
The patent segments the information extraction process into distinct modules: image processing module that extracts visual features (colors, patterns, materials), text processing module that extracts descriptive attributes from product titles and descriptions, and a tag generation module that combines both. This segmentation allows each module to specialize in specific tasks, improving overall system efficiency while maintaining comprehensive data indexing
Solution Approach 2:
The search index structure is designed to be universal, accommodating multiple types of descriptors (visual attributes like colors and patterns, textual attributes from descriptions, and hierarchical categories). This multi-functional index can handle diverse search queries (by color, pattern, material, or text) without requiring separate processing systems for each query type
3Adaptability or versatility
If additional descriptive tags are generated from image data, then the range of matched information expands improving search relevance, but the processing time increases due to enhanced image analysis
Solution Approach 1:
The system performs image recognition and tag generation as a preliminary action during the product indexing phase, rather than during actual search operations. By extracting visual descriptors (colors, patterns, materials) upfront and storing them in the search index, the system expands the matchable information range while keeping search query processing fast, as the heavy image analysis is completed beforehand
Solution Approach 2:
The patent applies partial action by selectively extracting only the most relevant visual features (colors, patterns, materials) that are most useful for search queries, rather than analyzing every possible image attribute. This focused approach expands the search match range for the most important visual descriptors while minimizing processing time by avoiding exhaustive image analysis
Data Source
AI summary
Indexing data is disclosed. An image and a text data associated with a dataset are received. A tag is generated using one or more hierarchical classifiers. The image and the text data are input into at least one of the one or more hierarchical classifiers. A search index is generated based at least on the generated tag.