Image Relevance Index Using Visual Word Action Data
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Solution Overview
Problem
Conventional image search approaches are computationally intensive and often fail to provide users with relevant items and images matching their queries, leading to inefficient user experiences and increased system resource usage.
Innovation Solution
The implementation of a data-driven image search system that utilizes image ranking models and a visual word action index, which incorporates user behavioral data such as clicks, purchases, and video consumption to rank and match images, improving relevance and reducing resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image search approaches use similarity of different portions of an image to identify matching images, then image matching capability is improved, but computational resources and time consumption increase substantially
Solution Approach 1:
The system performs preliminary actions by pre-computing and storing visual words and their associations with images in a visual word action index during off-peak times. When a search query arrives, the system retrieves pre-computed visual words and their probabilities from the index rather than performing intensive image analysis in real-time, significantly reducing computational resource consumption while maintaining matching accuracy.
Solution Approach 2:
The patent replaces traditional mechanical image comparison systems with a data-driven approach using machine learning models and probability-based visual word matching. Instead of directly comparing image pixels and features computationally, the system substitutes this with retrieving and comparing visual word representations and their action probabilities from pre-computed indexes, reducing real-time computational burden.
2Device complexity
If conventional image search approaches rely solely on image similarity, then implementation simplicity is maintained, but relevance of search results to user interest deteriorates
Solution Approach 1:
The system merges traditional image similarity matching with user behavioral data by combining visual word matching results with action probabilities derived from user interactions (clicks, purchases, video consumption). This integration allows the system to maintain the simplicity of image-based search while enhancing result relevance through incorporation of user interest signals from multiple data sources.
Solution Approach 2:
The system implements feedback mechanisms by utilizing user action data (clicks, purchases, video consumption) to generate action probabilities that influence search result ranking. User interactions with search results feed back into the system to refine and update the visual word action index and machine learning models, continuously improving search result relevance based on actual user behavior patterns.
3Quantity of substance
If the system processes all images to provide comprehensive search results, then result completeness is improved, but system resource usage and processing time increase
Solution Approach 1:
The system applies partial action by processing and indexing only the most discriminative visual words and their associated action probabilities rather than analyzing every pixel and feature of all images. The visual word action index stores condensed representations that capture essential matching information, allowing the system to provide comprehensive search results using a subset of processed data, thereby reducing processing time while maintaining result quality.
Data Source
AI summary
The selection of content to present to a user can be based at least in part upon probabilities of the user selecting to view more information and/or entering into a transaction with respect to instances of the content. For example, user behavior with respect to various queries provided through a content provider can be determined in order to calculate a probability that a user was searching for a particular image. The user behavior can include historical action data, such as information that indicates images associated with an action (e.g., selected, purchased, etc.) in response to a particular image search. The historical action data can be analyzed to generate an index that indicates a likelihood that the search was intended for a particular image. Once an image query is received, items of interest can be determined using the index, and those images and associated content can be presented to the user.