Recognizing clothes patterns for blind people by confidence margin based feature combination

Authors:
Xiaodong Yang;Shuai Yuan;YingLi Tian
Affiliations:
The City College of New York, CUNY, New York, NY, USA;The City College of New York, CUNY, New York, USA;The City College of New York, CUNY, New York, USA
Venue:
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Year:
2011

Citing 10
Cited 2

The nature of statistical learning theory

The nature of statistical learning theory
Texture classification by center-symmetric auto-correlation, using Kullback discrimination of distributions

Pattern Recognition Letters
Filtering for Texture Classification: A Comparative Study

IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints

International Journal of Computer Vision
A Sparse Texture Representation Using Local Affine Regions

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Performance Evaluation of Local Descriptors

IEEE Transactions on Pattern Analysis and Machine Intelligence
Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study

International Journal of Computer Vision
Sampling strategies for bag-of-features image classification

ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Improving generalization by data categorization

PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Texture Analysis and Classification With Linear Regression Model Based on Wavelet Transform

IEEE Transactions on Image Processing

Camera-Based signage detection and recognition for blind persons

ICCHP'12 Proceedings of the 13th international conference on Computers Helping People with Special Needs - Volume Part II
Texture representations using subspace embeddings

Pattern Recognition Letters

Quantified Score

Hi-index	0.00

Visualization

Abstract

Clothes pattern recognition is a challenging task for blind or visually impaired people. Automatic clothes pattern recognition is also a challenging problem in computer vision due to the large pattern variations. In this paper, we present a new method to classify clothes patterns into 4 categories: stripe, lattice, special, and patternless. While existing texture analysis methods mainly focused on textures varying with distinctive pattern changes, they cannot achieve the same level of accuracy for clothes pattern recognition because of the large intra-class variations in each clothes pattern category. To solve this problem, we extract both structural feature and statistical feature from image wavelet subbands. Furthermore, we develop a new feature combination scheme based on the confidence margin of a classifier to combine the two types of features to form a novel local image descriptor in a compact and discriminative format. The recognition experiment is conducted on a database with 627 clothes images of 4 categories of patterns. Experimental results demonstrate that the proposed method significantly outperforms the state-of-the-art texture analysis methods in the context of clothes pattern recognition.