Query Object Segmentation via Spatially Constrained Similarity
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Solution Overview
Problem
Conventional bag-of-words models for image and object retrieval lack spatial information, leading to poor performance in applications with varying lighting, viewpoint changes, and cluttered backgrounds, and struggle with non-planar and less-textured objects, resulting in inaccurate segmentation and identification.
Innovation Solution
A method for query object localization and segmentation that uses spatially constrained similarity measures to identify and retrieve images from a database, estimate the object location, and iteratively refine the segmentation, reducing the influence of background clutter and improving retrieval accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If bag-of-words model is used for image retrieval, then feature representation and indexing efficiency are improved, but spatial information is lost
Solution Approach 1:
The patent segments the image into multiple spatial regions (e.g., foreground, background, different depth layers) and applies different processing strategies to each region. This allows spatial information to be preserved through region-specific feature extraction and matching, while still benefiting from the efficient bag-of-words indexing framework.
2Speed
If feature quantization is applied in BOW model, then indexing speed is improved, but discriminative power of features is reduced
Solution Approach 1:
The patent applies different quantization strategies to different spatial regions and feature types. High-discriminative features in critical regions (e.g., object boundaries, distinctive textures) are preserved with finer quantization, while less critical features use coarser quantization for faster indexing. This local differentiation maintains overall discriminative power while improving indexing efficiency.
3Measurement precision
If manual labeling is used for query object segmentation, then segmentation accuracy is improved, but user burden increases
Solution Approach 1:
The patent implements automatic query object segmentation using computer vision algorithms that analyze the query image to identify and segment the target object without user intervention. The system uses techniques such as edge detection, region segmentation, and object recognition to automatically generate accurate masks, eliminating the need for manual user labeling while maintaining high segmentation accuracy.
4Speed
If BOW model processes query images with cluttered backgrounds, then retrieval speed is maintained, but retrieval accuracy deteriorates
Solution Approach 1:
The patent extracts and isolates the query object from the cluttered background through automatic segmentation, creating a clean object mask. This extracted object representation is then used for retrieval, excluding distracting background elements. The system processes only the relevant object features while maintaining the efficiency of the BOW model, thereby improving retrieval accuracy without sacrificing speed.
Data Source
AI summary
Query object localization, segmentation, and retrieval are disclosed. A query image may be received that includes a query object. Based on respective spatially constrained similarity measures between the query image and a plurality of images from an image database, at least some of the plurality of images may be identified and/or retrieved and a location of the query object in the query image may be estimated. The query object may then be automatically segmented from the query image based on the estimated query object location. In some embodiments, the retrieval, localization and/or segmentation may be iterated.