A Textural Feature-Based Image Retrieval Algorithm

This paper firstly studies the texture features. We construct a gray-difference primitive co-occurrence matrix to extract texture features by combining statistical methods with structural ones. The experiment results show that the features of the gray-difference primitive co-occurrence matrix are more delicate than the traditional gray co-occurrence matrix.

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A Medical Image Retrieval Algorithm Based on DFT Encryption Domain

Smart Health - Lecture Notes in Computer Science ◽

10.1007/978-3-319-59858-1_20 ◽

2017 ◽

pp. 205-216

Author(s):

Chunyan Zhang ◽

Jingbing Li ◽

Shuangshuang Wang ◽

Yucong Duan ◽

Mengxing Huang ◽

...

Keyword(s):

Image Retrieval ◽

Medical Image ◽

Retrieval Algorithm ◽

Medical Image Retrieval

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Performance Evaluation of Single-Label and Multi-Label Remote Sensing Image Retrieval Using a Dense Labeling Dataset

Remote Sensing ◽

10.3390/rs10060964 ◽

2018 ◽

Vol 10 (6) ◽

pp. 964 ◽

Cited By ~ 34

Author(s):

Zhenfeng Shao ◽

Ke Yang ◽

Weixun Zhou

Keyword(s):

Remote Sensing ◽

Performance Evaluation ◽

Deep Learning ◽

Image Retrieval ◽

Semantic Segmentation ◽

Semantic Content ◽

Remote Sensing Image ◽

Remote Sensing Images ◽

Benchmark Datasets ◽

Feature Based

Benchmark datasets are essential for developing and evaluating remote sensing image retrieval (RSIR) approaches. However, most of the existing datasets are single-labeled, with each image in these datasets being annotated by a single label representing the most significant semantic content of the image. This is sufficient for simple problems, such as distinguishing between a building and a beach, but multiple labels and sometimes even dense (pixel) labels are required for more complex problems, such as RSIR and semantic segmentation.We therefore extended the existing multi-labeled dataset collected for multi-label RSIR and presented a dense labeling remote sensing dataset termed "DLRSD". DLRSD contained a total of 17 classes, and the pixels of each image were assigned with 17 pre-defined labels. We used DLRSD to evaluate the performance of RSIR methods ranging from traditional handcrafted feature-based methods to deep learning-based ones. More specifically, we evaluated the performances of RSIR methods from both single-label and multi-label perspectives. These results demonstrated the advantages of multiple labels over single labels for interpreting complex remote sensing images. DLRSD provided the literature a benchmark for RSIR and other pixel-based problems such as semantic segmentation.

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