Aesthetic Search Engine Using Image Feature Classification
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Solution Overview
Problem
Users face challenges in selecting aesthetically pleasing arrangements of items for design purposes, such as interior or clothing, as they cannot physically inspect items together, relying solely on images and descriptions, which limits their ability to create harmonious combinations.
Innovation Solution
An aesthetic search system that analyzes designer images, performs feature detection and classification, and generates search results based on similarity and aesthetic parameters, allowing users to find items that match the style and arrangement of selected items, using a networked system with client-server architecture and machine learning algorithms for accurate matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users rely solely on images and descriptions to select items, then the ease of operation is improved, but the measurement precision of aesthetic judgment deteriorates
Solution Approach 1:
The system creates virtual copies of physical item arrangements through designer images, allowing users to view and analyze aesthetic combinations without physically handling items. The image analysis system extracts features from these visual copies to enable automated aesthetic matching and item recommendation.
2Measurement precision
If users physically inspect items in a showroom, then the measurement precision of aesthetic judgment is improved, but the loss of time increases
Solution Approach 1:
Designers pre-analyze and curate item arrangements in showroom settings, creating labeled images with aesthetic features extracted beforehand. This preliminary action allows users to later query and retrieve pre-processed aesthetic combinations without needing to physically visit showrooms, saving significant time while maintaining judgment accuracy.
Solution Approach 2:
The system introduces an image analysis system as an intermediary between physical item arrangements and user decision-making. This intermediary automatically extracts aesthetic features from designer images and enables automated matching, replacing the need for users to physically inspect items while preserving aesthetic judgment quality.
3Adaptability or versatility
If users create arrangements of multiple items, then the complexity of the task increases, but the need for aesthetic coordination becomes more critical
Solution Approach 1:
The image analysis system serves as an intermediary that automatically extracts aesthetic features from multiple items in arrangements and performs similarity matching. This mediator handles the complex task of coordinating multiple items aesthetically, allowing users to leverage pre-analyzed designer arrangements without manually managing the complexity of item coordination.
Solution Approach 2:
The system provides feedback to users by returning search results that match the aesthetic features of their selected items. This feedback mechanism helps users iteratively build and refine item arrangements by showing them comparable items from designer arrangements, making the complex task of creating coordinated arrangements more manageable.
Data Source
AI summary
An improved approach for returning aesthetically relevant search results is disclosed. A training set of images (e.g., designer-created images) is used to train a detection engine that detects items in the images as features. A classification engine is configured to analyze the features and generate classification indices for the features. A user can select an item, and the classification index for the feature corresponding to the item is retrieved. The classification index is used to identify result images, which can be returned ranked according user action data and other parameters, such as style.