Saliency Map Based Image Crop Prioritization
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Solution Overview
Problem
Traditional image retrieval systems face challenges in efficiently identifying and presenting the most relevant images from large collections to users, often resulting in high system latency and requiring significant user effort to find desired images, as they struggle to accurately match search queries with relevant image content.
Innovation Solution
A computer-based system that maps input images to saliency maps to identify objects and their locations, allowing for the autocropping of salient regions, which are then used to generate prioritized search results, either by indexing pre-cropped images or generating crops at runtime, thereby focusing on the most relevant parts of images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional image retrieval systems return full images from large collections, then users can see complete image content, but system latency increases and users must spend more time searching for relevant portions
Solution Approach 1:
The system segments images into multiple crops based on saliency detection, identifying and extracting the most relevant portions of images while discarding less important regions. This segmentation approach allows the system to return smaller, more focused image crops that reduce transmission and processing time while maintaining the essential informative content that users need.
Solution Approach 2:
The system extracts only the salient or most relevant portions of images using saliency maps and autocropping algorithms. By taking out and returning only the essential image content rather than complete images, the system reduces data transmission requirements and display time while preserving the key information users are seeking.
2Productivity
If the system returns cropped images focusing on salient regions, then system latency is reduced and relevance is improved, but the complexity of image processing increases
Solution Approach 1:
The system performs preliminary saliency detection and autocropping operations during the image indexing phase before users submit queries. By pre-processing images to identify and crop salient regions in advance, the system avoids performing complex processing at query time, thereby reducing operational complexity during actual use while maintaining high retrieval efficiency.
Solution Approach 2:
The system introduces saliency maps as an intermediary representation between the original image and the final cropped output. These maps serve as a computational bridge that guides the autocropping process, allowing the system to systematically identify salient regions without requiring complex ad-hoc processing for each query.
3Measurement precision
If the system processes and crops images at runtime, then relevant image content is provided quickly, but additional processing time is required
Solution Approach 1:
The system performs image cropping and saliency detection during the offline indexing phase, generating pre-cropped image versions before users submit queries. This preliminary processing ensures that when queries are submitted, the system can quickly retrieve and display relevant cropped images without performing time-consuming processing at runtime, thereby achieving both high precision and fast response.
Data Source
AI summary
Various aspects of the subject technology relate to systems, methods, and machine-readable media for identification of a salient portion of an image. A system may receive user input identifying a search query for content from a client device. The system may determine a listing of images responsive to the search query from an image collection. The system may obtain one or more image crops for at least one image of the listing of images based on a saliency map of the at least one image. In one or more implementations, each of the one or more image crops indicates a salient region of a corresponding image. The system may provide a set of search results responsive to the search query to the client device. In one or more implementations, the set of search results includes the obtained one or more image crops in a prioritized listing of image crops.