Reverse Image Search with Manual Composition Refinement
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional image retrieval systems face challenges in identifying the most relevant images by composition, especially in large collections, as users often struggle to find images that match their mental conception of a specific composition.
Innovation Solution
A computer-implemented method and system that allows users to describe a scene or layout as a compositional input, enabling refinement of existing image search results or initiation of new searches. This is facilitated through a user interface that enables users to specify compositions, add elements, and apply masks or adjustments to images, using a convolutional neural network to classify salient objects and generate metadata for image compositions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional image retrieval systems search through large collections of images, then the quantity of images available increases, but the difficulty of finding images that match specific compositional requirements increases
Solution Approach 1:
The patent segments the image composition into distinct visual elements (foreground objects, background, spatial relationships) that can be independently analyzed and searched. The system breaks down complex compositional queries into separate detectable features, allowing the retrieval system to evaluate each element independently and combine results, thereby making composition-based search feasible even in large image collections.
Solution Approach 2:
The patent introduces an intermediary compositional analysis system that acts as a mediator between the user's compositional query and the image database. This intermediary layer analyzes images for specific compositional features (object positions, relationships, spatial arrangements) and translates complex compositional descriptions into searchable parameters, bridging the gap between human compositional understanding and machine-based image retrieval.
2Measurement precision
If the system performs detailed compositional analysis of images, then search accuracy improves, but processing time and system complexity increase
Solution Approach 1:
The patent applies preliminary action by pre-processing images to detect and store compositional features (objects, positions, relationships) in advance. During search operations, the system retrieves and compares these pre-extracted features rather than performing full compositional analysis on each image, significantly reducing processing time while maintaining search accuracy. The system prepares compositional data structures ahead of time for faster query response.
Solution Approach 2:
The patent applies local quality by focusing compositional analysis only on relevant regions and elements of images based on the specific query. Rather than analyzing entire images uniformly, the system identifies and analyzes only the specific objects, regions, or compositional aspects mentioned in the query, reducing processing overhead while maintaining precision for the queried features.
3Adaptability or versatility
If the system stores detailed compositional metadata for each image, then compositional search capability improves, but data storage requirements increase
Solution Approach 1:
The patent extracts only the essential compositional features needed for search functionality (object positions, spatial relationships, key elements) and stores them as compact metadata structures. Rather than storing complete image data or exhaustive compositional descriptions, the system extracts and stores only the critical compositional parameters required for query matching, reducing storage requirements while maintaining search capability.
Solution Approach 2:
The patent transforms compositional information from the image space into a separate metadata dimension that can be efficiently stored and queried. By representing compositional features as structured data (coordinates, relationships, object identifiers) rather than storing actual image pixels or complex visual descriptions, the system enables compositional search without proportionally increasing storage requirements.
Data Source
AI summary
Various aspects of the subject technology relate to systems, methods, and machine-readable media for reverse search with manual composition. A system generates a first search result associated with a forward image search that is responsive to a search query from a client device, and each image in the first search result respectively includes a base layer that includes a representation of a first composition for the image. The system may receive user input indicating a target composition defined by the base layer of at least one image from the first search result and an object layer that indicates adjustments to the first composition of the base layer, and generates a second search result using the target composition. The second search result includes second images that respectively include a representation of a second composition that corresponds to the target composition. The system provides the second search result to the client device.