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

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

doi:10.1155/2014/439218

Cited by 30 publications

(9 citation statements)

References 18 publications

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“…One of the driving factors for inappropriate output has been due to reprehensible selection of keywords as query. Recent approaches of searching have emphasized on the content of the searched object rather than its name as a keyword [9][10][11][12]. The content based searching process has been facilitated by the product image which can provide the necessary knowledge for the required product based on its image contents and has been anticipated to filter out the unwanted results with higher probability.…”

Section: Related Workmentioning

confidence: 99%

“…Feature extraction with mean threshold for binarization has been discussed in [19,20] and with bit plane slicing in [21]. The problem of uneven illumination in images was efficiently addressed by local threshold techniques [9,[22][23][24][25][26]. The literatures have used measures of dispersion like standard deviation and variance to calculate the threshold.…”

Section: Related Workmentioning

confidence: 99%

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Retrieval Architecture with Classified Query for Content Based Image Recognition

Das

Thepade²,

Bhattacharya

et al. 2016

Applied Computational Intelligence and Soft Computing

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The consumer behavior has been observed to be largely influenced by image data with increasing familiarity of smart phones and World Wide Web. Traditional technique of browsing through product varieties in the Internet with text keywords has been gradually replaced by the easy accessible image data. The importance of image data has portrayed a steady growth in application orientation for business domain with the advent of different image capturing devices and social media. The paper has described a methodology of feature extraction by image binarization technique for enhancing identification and retrieval of information using content based image recognition. The proposed algorithm was tested on two public datasets, namely, Wang dataset and Oliva and Torralba (OT-Scene) dataset with 3688 images on the whole. It has outclassed the state-of-the-art techniques in performance measure and has shown statistical significance.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Retrieval Architecture with Classified Query for Content Based Image Recognition

Das

Thepade²,

Bhattacharya

et al. 2016

Applied Computational Intelligence and Soft Computing

Self Cite

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“…Transform-domain techniques have readily contributed to rich feature extraction from image content [31]. Recent techniques have shown proficiency in image identification with relevant features [32], [33]. Color and texture were calculated as local descriptors from color moments and moments of Gabor filter responses for higher retrieval performance [34].…”

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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“…Traditional Otsu's method of global threshold selection [16,17] was based on image variance. Local threshold selection techniques [18,19,20,21,22,23,24] have considered image variance and contrast as factors for image binarization. But the image recognition process was slowed down due to hefty feature vector size generated by the majority of the aforesaid procedures.…”

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

Cited by 30 publications

References 18 publications

Retrieval Architecture with Classified Query for Content Based Image Recognition

Retrieval Architecture with Classified Query for Content Based Image Recognition

Framework for Content-Based Image Identification with Standardized Multiview Features

Content Based Image Recognition by Information Fusion with Multiview Features

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