1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.
10 years of news and resources for members of the IEEE Signal Processing Society
Yang, Fan. (University of Maryland, College Park), “Leveraging Multiple Features for Image Retrieval and Matching” (2016) Advisor: Davis, Larry S.
The goal of image retrieval and matching is to find and locate object instances in images from a large-scale image database. While visual features are abundant, how to combine them to improve performance by individual features remains a challenging task. In this work, the authors focus on leveraging multiple features for accurate and efficient image retrieval and matching.
The authors first propose two graph-based approaches to rerank initially retrieved images for generic image retrieval. In the graph, vertices are images while edges are similarities between image pairs. Their first approach employs a mixture Markov model based on a random walk model on multiple graphs to fuse graphs. The authors introduce a probabilistic model to compute the importance of each feature for graph fusion under a naive Bayesian formulation, which requires statistics of similarities from a manually labeled dataset containing irrelevant images. To reduce human labeling, the authors further propose a fully unsupervised reranking algorithm based on a submodular objective function that can be efficiently optimized by greedy algorithm. By maximizing an information gain term over the graph, their submodular function favors a subset of database images that are similar to query images and resemble each other. The function also exploits the rank relationships of images from multiple ranked lists obtained by different features.
The authors then study a more well-defined application, person re-identification, where the database contains labeled images of human bodies captured by multiple cameras. Re-identifications from multiple cameras are regarded as related tasks to exploit shared information. The authors apply a novel multi-task learning algorithm using both low level features and attributes. A low rank attribute embedding is joint learned within the multi-task learning formulation to embed original binary attributes to a continuous attribute space, where incorrect and incomplete attributes are rectified and recovered.
To locate objects in images, the authors design an object detector based on object proposals and deep convolutional neural networks (CNN) in view of the emergence of deep networks. The authors improve a Fast RCNN framework and investigate two new strategies to detect objects accurately and efficiently: scale-dependent pooling (SDP) and cascaded rejection classifiers (CRC). The SDP improves detection accuracy by exploiting appropriate convolutional features depending on the scale of input object proposals. The CRC effectively utilizes convolutional features and greatly eliminates negative proposals in a cascaded manner, while maintaining a high recall for true objects. The two strategies together improve the detection accuracy and reduce the computational cost.
|Nominations Open for 2020 SPS Awards||1 September 2020|
|Call for Nominations: Awards Board and Nominations and Appointments Committee||25 September 2020|
|Call for Nominations: Fellow Evaluation Committee||30 September 2020|
|Election of Regional Directors-at-Large and Members-at-Large||1 October 2020|
|Meet the 2020 Candidates: IEEE President-Elect and Division IX Director-Elect||1 October 2020|
|Call for Nominations: SPS Chapter of the Year Award||15 October 2020|
|Call for Nominations Extended: Chair, Young Professionals Committee||16 October 2020|
© Copyright 2020 IEEE – All rights reserved. Use of this website signifies your agreement to the IEEE Terms and Conditions.
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.