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9 Result(s)
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Article
Zero-shot classification with unseen prototype learning
Zero-shot learning (ZSL) aims at recognizing instances from unseen classes via training a classification model with only seen data. Most existing approaches easily suffer from the classification bias from unse...
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Book
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Chapter
Deep Learning in Object Detection
Object detection is an important research area in image processing and computer vision. The performance of object detection has significantly improved through applying deep learning technology. Among these met...
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Chapter and Conference Paper
Cross-Modal Retrieval with Discriminative Dual-Path CNN
Cross-modal retrieval aims at searching semantically similar examples in one modality by using a query from another modality. Its typical applications including image-based text retrieval (IBTR) and text-based...
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Chapter and Conference Paper
Zero-Shot Learning with Deep Canonical Correlation Analysis
Zero-shot learning (ZSL) improves the scalability of conventional image classification systems by allowing some testing categories having no training data. One key component is to learn a shared embedding spac...
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Chapter and Conference Paper
Enhancement for Dust-Sand Storm Images
A novel dust-sand storm image enhancement scheme is proposed. The input degraded color image is first convert into CIELAB color space. Then two chromatic components (a* and b*) are combined to perform color cast ...
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Chapter and Conference Paper
Marginal Fisher Regression Classification for Face Recognition
This paper presents a novel marginal Fisher regression classification (MFRC) method by incorporating the ideas of marginal Fisher analysis (MFA) and linear regression classification (LRC). The MFRC aims at minimi...
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Chapter and Conference Paper
Balance between Diversity and Relevance for Image Search Results
Image search reranking has received great attention since it overcomes the drawback of “only textual features utilization” in nowadays web-scale image search engines. Most of existing methods focus on relevanc...
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Chapter and Conference Paper
Image Search Reranking with Semi-supervised LPP and Ranking SVM
Learning to rank is one of the most popular ranking methods used in image retrieval and search reranking. However, the high-dimension of the visual features usually causes the problem of “curse of dimensionali...
