Performance comparison of feature vector extraction techniques in RGB color space using block truncation coding for content based image classification with discrete classifiers

Thepade, Sudeep D.; Das, Rik; Ghosh, Swaroop

doi:10.1109/indcon.2013.6726053

Cited by 33 publications

(21 citation statements)

References 14 publications

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“…Selection of both mean threshold and multilevel mean threshold for feature extraction with binarization has revealed improved classification results in six different color spaces [15]. Extraction of features has been carried out using mean threshold for binarization of significant bit planes of images and from even and odd image varieties for better image classification [16], [17]. The problem is that a mean-threshold selection technique considers only the average of the gray values and not the standard deviation.…”

Section: Related Workmentioning

confidence: 99%

Framework for Content-Based Image Identification with Standardized Multiview Features

Das

Thepade²,

Ghosh

2016

ETRI J

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Information identification with image data by means of low‐level visual features has evolved as a challenging research domain. Conventional text‐based mapping of image data has been gradually replaced by content‐based techniques of image identification. Feature extraction from image content plays a crucial role in facilitating content‐based detection processes. In this paper, the authors have proposed four different techniques for multiview feature extraction from images. The efficiency of extracted feature vectors for content‐based image classification and retrieval is evaluated by means of fusion‐based and data standardization–based techniques. It is observed that the latter surpasses the former. The proposed methods outclass state‐of‐the‐art techniques for content‐based image identification and show an average increase in precision of 17.71% and 22.78% for classification and retrieval, respectively. Three public datasets — Wang; Oliva and Torralba (OT‐Scene); and Corel — are used for verification purposes. The research findings are statistically validated by conducting a paired t‐test.

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Section: Related Workmentioning

confidence: 99%

Framework for Content-Based Image Identification with Standardized Multiview Features

Das

Thepade²,

Ghosh

2016

ETRI J

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“…Extraction of features from bit planes and even and odd image varieties has been performed by mean threshold selection for better classification results [12,13]. Feature extraction using ternary mean threshold [14] and multilevel mean threshold [15] for binarization has also considered mean value of the grey levels for selecting the threshold for binarization.…”

Section: Related Workmentioning

confidence: 99%

Content Based Image Recognition by Information Fusion with Multiview Features

Das¹,

Thepade²,

Ghosh³

2015

IJITCS

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Abstract-Substantial research interest has been observed in the field of object recognition as a vital component for modern intelligent systems. Content based image classification and retrieval have been considered as two popular techniques for identifying the object of interest. Feature extraction has played the pivotal role towards successful implementation of the aforesaid techniques. The paper has presented two novel techniques of feature extraction from diverse image categories both in spatial domain and in frequency domain. The multi view features from the image categories were evaluated for classification and retrieval performances by means of a fusion based recognition architecture. The experimentation was carried out with four different popular public datasets. The proposed fusion framework has exhibited an average increase of 24.71% and 20.78% in precision rates for classification and retrieval respectively, when compared to state-of-the art techniques. The experimental findings were validated with a paired t test for statistical significance.

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“…Process of threshold selection has been categorized into three different techniques, namely, mean threshold selection, local threshold selection, and global threshold selection. Existing methods of feature extraction from images using selection of mean threshold were adopted by Thepade et al in [2] and by Kekre et al in [3]. The first method of feature extraction using even and odd images [2] 2 Journal of Engineering has generated two different varieties of images by adding and subtracting the original image and its flipped version, respectively, for each variety as shown in even = ( + ) 2 ,…”

Section: Related Workmentioning

confidence: 99%

“…In this work, a new feature extraction technique applying binarization on bit planes using local threshold technique has been proposed. A digital image can be separated into bit planes to understand the importance of each bit in the image as shown by Thepade et al in [2]. The process was followed by binarization of significant bit planes for feature vector extraction.…”

Section: Introductionmentioning

confidence: 99%

“…Binarization process calculated the threshold value to differentiate the object of interest from its background. The novel method has been compared quantitatively with the techniques proposed by Thepade et al in [2] and by Kekre et al in [3] and four other widely used image binarization techniques proposed by Niblack [4], Bernsen [5], Sauvola and Pietikäinen [6], and Otsu [7]. Mean square error (MSE) method was followed for classification performance evaluation of the proposed technique with respect to the existing techniques for feature vector extraction.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Feature Extraction Technique Using Binarization of Bit Planes for Content Based Image Classification

Thepade¹,

Das²,

Ghosh

2014

Journal of Engineering

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A number of techniques have been proposed earlier for feature extraction using image binarization. Efficiency of the techniques was dependent on proper threshold selection for the binarization method. In this paper, a new feature extraction technique using image binarization has been proposed. The technique has binarized the significant bit planes of an image by selecting local thresholds. The proposed algorithm has been tested on a public dataset and has been compared with existing widely used techniques using binarization for extraction of features. It has been inferred that the proposed method has outclassed all the existing techniques and has shown consistent classification performance.

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Performance comparison of feature vector extraction techniques in RGB color space using block truncation coding for content based image classification with discrete classifiers

Cited by 33 publications

References 14 publications

Framework for Content-Based Image Identification with Standardized Multiview Features

Framework for Content-Based Image Identification with Standardized Multiview Features

Content Based Image Recognition by Information Fusion with Multiview Features

A Novel Feature Extraction Technique Using Binarization of Bit Planes for Content Based Image Classification

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