Business Venue Image Association via Semantic Concept Detection
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Solution Overview
Problem
Conventional methods for identifying business venues in images are inefficient, as they often rely on coarse-grain locations, database image comparisons, or low-level visual patterns, failing to accurately associate images with specific business venues, especially when sparse data is available.
Innovation Solution
The implementation uses a framework that combines text-based reviews and stored images to identify business-aware concepts in images, employing trained visual detectors and word representation models to associate images with business venues, even in the absence of explicit geotags or unique visual patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional methods use coarse-grain locations or database image comparisons to identify business venues, then the system complexity is reduced, but the measurement precision of business venue recognition deteriorates
Solution Approach 1:
The patent combines multiple data modalities including text-based reviews, stored images, and visual detectors into a unified framework. This integration allows the system to leverage diverse information sources (textual semantics from reviews, visual patterns from images, and detected visual concepts) to improve recognition accuracy without requiring a single complex system component
Solution Approach 2:
The patent introduces a new dimensional approach by mining semantic concepts from textual reviews and converting them to word representations (word vectors). This adds a textual/semantic dimension to the traditional image-based matching approach, enabling the system to compare images with business venues using both visual and textual features, thereby improving precision without proportionally increasing system complexity
2Ease of operation
If conventional methods rely on database image comparisons to match images with business venues, then the ease of operation is improved, but the reliability of venue identification deteriorates when sparse data is available
Solution Approach 1:
The patent introduces semantic concepts as intermediaries between images and business venues. Instead of directly comparing images with venues, the system first detects visual concepts in images, then matches these concepts with semantic concepts extracted from business venue reviews. This intermediary layer enables reliable matching even when direct image-venue pairs are unavailable in the database
Solution Approach 2:
The system performs preliminary mining of semantic concepts from textual reviews and pre-computes word representations for business venues before actual image matching occurs. This preliminary preparation creates a robust reference framework that enables reliable venue identification even when query images have sparse data, as the semantic concept database is already populated with comprehensive business venue information
3Device complexity
If conventional methods use low-level visual patterns to identify business venues, then the device complexity is reduced, but the measurement precision deteriorates due to inability to differentiate between general consumer images
Solution Approach 1:
The patent applies local quality by training visual detectors to identify specific business-aware concepts at different levels of abstraction. Instead of using uniform low-level visual patterns across all images, the system employs specialized detectors tuned to recognize domain-specific visual features relevant to different business venue types, improving differentiation accuracy while maintaining manageable complexity through targeted detection
Data Source
AI summary
Systems and methods disclosed herein associate images with business venues. An example method includes: receiving a first image and retrieving textual reviews and stored images that are associated with one or more candidate business venues. The method further includes: detecting, using trained visual detectors, a plurality of business-aware concepts in the first image and assessing likelihood that detected business-aware concepts are in the first image. The method additionally includes: (i) generating a first representation of the first image based on the likelihoods and one or more term vectors for high-scoring concepts and (ii) receiving second representations of each candidate based on the retrieved textual reviews and stored images. In accordance with determining that the first representation is most similar to a respective second representation of a first candidate, the method includes: (i) associating the first image with the first candidate and (ii) providing an indication of the association.