Journal ArticleDOI
Saliency-based multi-feature modeling for semantic image retrieval
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TLDR
An approach integrating visual saliency model with BOW is proposed for semantic image retrieval and the results evaluated in terms of mean Average Precision show that this proposal outperforms the referred state-of-the-art approaches.About:
This article is published in Journal of Visual Communication and Image Representation.The article was published on 2018-01-01. It has received 46 citations till now. The article focuses on the topics: Image retrieval & Semantic gap.read more
Citations
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Journal ArticleDOI
Optimization of deep convolutional neural network for large scale image retrieval
TL;DR: The proposed framework optimizes AlexNet in three aspects: pooling layer, fully connected layer and hidden layer, and outperforms state-of-the-art methods on public databases for image retrieval, including large scale database.
Journal ArticleDOI
Content-based image retrieval: A review of recent trends
TL;DR: Survey, analyses and compares the current state-of-the-art methodologies over the last six years in the CBIR field, and provides an overview of CBIR framework, recent low-level feature extraction methods, machine learning algorithms, similarity measures, and a performance evaluation to inspire further research efforts.
Proceedings ArticleDOI
Deep Adversarial Discrete Hashing for Cross-Modal Retrieval
TL;DR: The proposed DADH improves the performance and outperforms several state-of-the-art hashing methods for cross-modal retrieval and introduces a weighted cosine triplet constraint which can make full use of semantic knowledge from the multi-label to ensure the precise ranking relevance of item pairs.
Journal ArticleDOI
Co-saliency detection via integration of multi-layer convolutional features and inter-image propagation
TL;DR: Experimental results on two public datasets demonstrate that the proposed convolutional neural network based co-saliency detection model achieves the best performance compared to the state-of-the-art co- saliency detection models.
Journal ArticleDOI
Radar Target Recognition using Salient Keypoint Descriptors and Multitask Sparse Representation
TL;DR: To characterize the targets in the radar images, the scale-invariant feature transform (SIFT) and the saliency map are combined to reduce the number of SIFT keypoints by keeping only those located in the target area (salient region); this speeds up the recognition process.
References
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Journal ArticleDOI
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Journal ArticleDOI
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
TL;DR: A generalized gray-scale and rotation invariant operator presentation that allows for detecting the "uniform" patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis.
Proceedings ArticleDOI
Video Google: a text retrieval approach to object matching in videos
TL;DR: An approach to object and scene retrieval which searches for and localizes all the occurrences of a user outlined object in a video, represented by a set of viewpoint invariant region descriptors so that recognition can proceed successfully despite changes in viewpoint, illumination and partial occlusion.
Journal ArticleDOI
Efficient Graph-Based Image Segmentation
TL;DR: An efficient segmentation algorithm is developed based on a predicate for measuring the evidence for a boundary between two regions using a graph-based representation of the image and it is shown that although this algorithm makes greedy decisions it produces segmentations that satisfy global properties.
Journal ArticleDOI
"GrabCut": interactive foreground extraction using iterated graph cuts
TL;DR: A more powerful, iterative version of the optimisation of the graph-cut approach is developed and the power of the iterative algorithm is used to simplify substantially the user interaction needed for a given quality of result.
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