Block-Based Image Pyramid Computation for Real-Time Visual Search
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Solution Overview
Problem
Existing image processing techniques for visual search and retrieval are inefficient under real-time constraints, particularly in handling high-resolution images, due to poor parallelizability and precision issues in mathematical processing.
Innovation Solution
The method involves computing an image pyramid in a transformed space using a block-based approach, where images are processed in blocks and filtered using a set of filters, allowing for efficient interest-point detection and descriptor extraction without reconstructing the full image pyramid, thus reducing memory requirements and improving processing speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image processing techniques are used to compute image pyramids from high-resolution images, then processing precision is maintained, but processing speed decreases and real-time constraints cannot be met
Solution Approach 1:
The image is divided into blocks of size MxM pixels, and processing is performed independently on each block. This segmentation allows parallel processing of multiple blocks simultaneously, significantly improving processing speed while maintaining the same filtering precision as traditional full-image methods.
2Reliability
If full image pyramid reconstruction is performed, then complete multi-scale representation is achieved, but memory requirements increase
Solution Approach 1:
Instead of reconstructing the complete image pyramid in memory, the method extracts only the necessary filtered block data at each scale level. The filtering operations are performed directly on blocked regions, and only essential intermediate results are retained, dramatically reducing memory requirements while preserving the multi-scale representation needed for interest-point detection.
3Measurement precision
If high-resolution images are processed using conventional methods, then feature extraction accuracy is maintained, but computational complexity increases
Solution Approach 1:
The high-resolution image is partitioned into MxM pixel blocks, allowing independent filtering operations on each block. This reduces computational complexity by enabling parallel processing and avoiding redundant calculations across the entire image, while maintaining feature extraction accuracy through consistent filtering applied to each block.
Solution Approach 2:
The method applies filtering operations selectively to blocked regions rather than processing the entire image uniformly at all scales. By performing filtering only on necessary blocks at appropriate resolution levels, the computational complexity is reduced while still capturing sufficient feature information for accurate interest-point detection.
Data Source
AI summary
An embodiment of a method for computing pyramids of input images (I) in a transformed domain, e.g., for search and retrieval purposes, includes:—arranging input images in blocks to produce input image blocks,—subjecting the input image blocks to block processing including: transform into a transformed domain, subjecting the image blocks transformed into a transformed domain to filtering, subjecting the image blocks transformed into a transformed domain and filtered to inverse transform implementing an inverse transform with respect to the previous transform into a transformed domain, thus producing a set of processed blocks. The set of processed blocks, which is recomposeable to an image pyramid, may be used, e.g., in detecting extrema points in images in the pyramid, extracting a patch of given size around the extrema points detected, and processing the patch to obtain local descriptors such as SIFT descriptors of a feature.