NDLI: An investigation of combining gradient descriptor and diverse classifiers to improve object taxonomy in very large image dataset

Content Provider	IEEE Xplore Digital Library
Author	Anusha, T.R Hemavathi, N. Mahantesh, K. Chetana, R.
Copyright Year	2014
Description	Author affiliation: Department of ECE, S.J.B Institute of Technology, Bangalore, India (Anusha, T.R; Hemavathi, N; Mahantesh, K; Chetana, R)
Abstract	Assigning a label pertaining to an image belonging to its category is defined as object taxonomy. In this paper, we propose a transform based descriptor which effectively extracts intensity gradients defining edge directions from segmented regions. Feature vectors comprising color, shape and texture information are obtained in compressed and de-correlated space. Firstly, Fuzzy c-means clustering is applied to an image in complex hybrid color space to obtain clusters based on color homogeneity of pixels. Further, HOG is employed on these clusters to extract discriminative features detecting local object appearance which is characterized with fine scale gradients at different orientation bins. To increase numerical stability, the obtained features are mapped onto local dimension feature space using PCA. For subsequent classification, diverse similarity measures and Neural networks are used to obtain an average correctness rate resulting in highly discriminative image classification. We demonstrated our proposed work on Caltech-101 and Caltech-256 datasets and obtained leading classification rates in comparison with several benchmarking techniques explored in literature.
Starting Page	581
Ending Page	585
File Size	525762
Page Count	5
File Format	PDF
ISBN	9781479966295
DOI	10.1109/IC3I.2014.7019774
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-11-27
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	HOG Vectors GRNN PCA Image segmentation Histograms PNN FCM Image color analysis Image Retrieval Neural networks Distance Measures Feature extraction Visual Descriptor Principal component analysis
Content Type	Text
Resource Type	Article

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