pdf/kcrc-lcd-discriminative-kernel-collaborative-representation-4gghnwjqvh.pdf

KCRC-LCD: Discriminative Kernel Collaborative Representation with Locality Constrained Dictionary for Visual Categorization

We consider the image classification problem via kernel collaborative representation classification with locality constrained dictionary (KCRC-LCD). Specifically, we propose a kernel collaborative representation classification (KCRC) approach in which kernel method is used to improve the discrimination ability of collaborative representation classification (CRC). We then measure the similarities between the query and atoms in the global dictionary in order to construct a locality constrained dictionary (LCD) for KCRC. In addition, we discuss several similarity measure approaches in LCD and further present a simple yet effective unified similarity measure whose superiority is validated in experiments. There are several appealing aspects associated with LCD. First, LCD can be nicely incorporated under the framework of KCRC. The LCD similarity measure can be kernelized under KCRC, which theoretically links CRC and LCD under the kernel method. Second, KCRC-LCD becomes more scalable to both the training set size and the feature dimension. Example shows that KCRC is able to perfectly classify data with certain distribution, while conventional CRC fails completely. Comprehensive experiments on many public datasets also show that KCRC-LCD is a robust discriminative classifier with both excellent performance and good scalability, being comparable or outperforming many other state-of-the-art approaches.

Optimized projection for Collaborative Representation based Classification and its applications to face recognition

Facial expression recognition (FER) plays a significant role in human-computer interaction. In this paper, adopting a dictionary learning feature space (DLFS) via sparse representation classification (SRC), we propose a method for FER. First, we obtain a difference dictionary (DD) from the feature space by indirectly using an auxiliary neutral training set. Next, we use a dictionary learning algorithm to train the DD; this algorithm considers the samples from the DD are approximately symmetrical structure. Finally, we use SRC to represent and determine the label of each query sample. We then verify out proposed method from the perspective of training samples, dimension reduction methods and Gaussian noise variances using a variety of public databases. In addition, we compare our DLFS_SRC approach with DLFS_CRC and DLFS_LRC approaches on the Extended Cohn-Kanade (CK+) database to analyze recognition results. Our simulation experiments show that our proposed method achieved satisfying performance levels for FER.

Dictionary learning feature space via sparse representation classification for facial expression recognition

We present a locality preserving K-SVD (LP-KSVD) algorithm for joint dictionary and classifier learning, and further incorporate kernel into our framework. In LP-KSVD, we construct a locality preserving term based on the relations between input samples and dictionary atoms, and introduce the locality via nearest neighborhood to enforce the locality of representation. Motivated by the fact that locality-related methods works better in a more discriminative and separable space, we map the original feature space to the kernel space, where samples of different classes become more separable. Experimental results show the proposed approach has strong discrimination power and is comparable or outperforms some state-of-the-art approaches on public databases.

/pdf/joint-kernel-dictionary-and-classifier-learning-for-sparse-2go378c6vx.pdf

Joint kernel dictionary and classifier learning for sparse coding via locality preserving K-SVD

Sparse representation classification (SRC) plays an important role in pattern recognition. Recently, a more generic method named as collaborative representation classification (CRC) has greatly improved the efficiency of SRC. By taking advantage of recent development of CRC, this paper explores to smoothly apply the kernel technique to further improve its performance and proposes the kernel CRC (KCRC) approach. Tested by multiple databases in experiments, KCR-C has shown that it can perfectly classify the data with the same direction distribution with limited complexity, and outperforms CRC, SRC and some other conventional algorithms.

A novel kernel collaborative representation approach for image classification

This paper deals with the design of variable bandwidth linear-phase FIR digital filters by weighted least square method. This proposed algorithm is constructed by a linear combination of fixed-coefficient linear-phase filters, of which the variable bandwidth characteristics are provided by an embedded tuning parameter. Using the weighted least square objective function, the optimal solution is obtained by solving a set of linear equation, which is an error function reflecting the difference between the desired variable bandwidth filter and the practical filter. The proposed algorithm can implement the variable digital filter efficiently by the familiar Farrow structure with only one variable spectral parameter that determines the bandwidth. Mathematics deduction and simulations show this algorithm is simple and effective in designing FIR variable digital filter with better performance than previous ones.

Algorithm of variable transition bandwidth FIR filters with weighted-least-square method

For the popular DIV page layout in Web Pages, this paper presents a method based on the position of DIV to extract main text from the body of Web pages by reconstructing, remaining atomic DIV and analyzing DIV position. Experiments showed that the accuracy rate of extraction can reach more than 90%, with a high versatility and accuracy.

http://ieeexplore.ieee.org/iel5/5445099/5451538/05451751.pdf

On Web Page extraction based on position of DIV

This paper considers the problem of applying network coding in Input and Output Queued (IOQ) packet switch loaded with both unicast and multicast flows. With network coding the switch can transmit the linear combination of multicast packets which prominently improves the switching efficiency in terms of throughput. The key idea is to use Vandermonde Matrix as the coding coefficient matrix while applying linear network coding over multicast packets. The paper also proposes a practical NC-based switch model to implement the coding approach.

Practical network coding approach for multicast packet switching

A Novel Transmission Scheme of Multiple Cognitive Relay Based on Network Coding: A Novel Transmission Scheme of Multiple Cognitive Relay Based on Network Coding

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Wei Wang

Author Tools

Chat about Author