141,164 research outputs found
Image databases: Problems and perspectives
With the increasing number of computer graphics, image processing, and pattern recognition applications, economical storage, efficient representation and manipulation, and powerful and flexible query languages for retrieval of image data are of paramount importance. These and related issues pertinent to image data bases are examined
Drawbacks and Proposed Solutions for Real-time Processing on Existing State-of-the-art Locality Sensitive Hashing Techniques
Nearest-neighbor query processing is a fundamental operation for many image
retrieval applications. Often, images are stored and represented by
high-dimensional vectors that are generated by feature-extraction algorithms.
Since tree-based index structures are shown to be ineffective for high
dimensional processing due to the well-known "Curse of Dimensionality",
approximate nearest neighbor techniques are used for faster query processing.
Locality Sensitive Hashing (LSH) is a very popular and efficient approximate
nearest neighbor technique that is known for its sublinear query processing
complexity and theoretical guarantees. Nowadays, with the emergence of
technology, several diverse application domains require real-time
high-dimensional data storing and processing capacity. Existing LSH techniques
are not suitable to handle real-time data and queries. In this paper, we
discuss the challenges and drawbacks of existing LSH techniques for processing
real-time high-dimensional image data. Additionally, through experimental
analysis, we propose improvements for existing state-of-the-art LSH techniques
for efficient processing of high-dimensional image data.Comment: Accepted and Presented at the 5th International Conference on Signal
and Image Processing (SIGI-2019), Dubai, UA
Cross-Reference Transformer for Few-shot Medical Image Segmentation
Due to the contradiction of medical image processing, that is, the
application of medical images is more and more widely and the limitation of
medical images is difficult to label, few-shot learning technology has begun to
receive more attention in the field of medical image processing. This paper
proposes a Cross-Reference Transformer for medical image segmentation, which
addresses the lack of interaction between the existing Cross-Reference support
image and the query image. It can better mine and enhance the similar parts of
support features and query features in high-dimensional channels. Experimental
results show that the proposed model achieves good results on both CT dataset
and MRI dataset.Comment: 6 pages,4 figure
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