A Hybrid Proposed Framework for Object Detection and Classification

The object classification using the images’ contents is a big challenge in computer vision. The superpixels’ information can be used to detect and classify objects in an image based on locations. In this paper, we proposed a methodology to detect and classify the image's pixels' locations...

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Veröffentlicht in:JIPS(Journal of Information Processing Systems) 2018-10, Vol.14 (5), p.1176-1194
Hauptverfasser: Aamir, Muhammad, Pu, Yi-Fei, Rahman, Ziaur, Abro, Waheed Ahmed, Naeem, Hamad, Ullah, Farhan, Badr, Aymen Mudheher
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container_end_page 1194
container_issue 5
container_start_page 1176
container_title JIPS(Journal of Information Processing Systems)
container_volume 14
creator Aamir, Muhammad
Pu, Yi-Fei
Rahman, Ziaur
Abro, Waheed Ahmed
Naeem, Hamad
Ullah, Farhan
Badr, Aymen Mudheher
description The object classification using the images’ contents is a big challenge in computer vision. The superpixels’ information can be used to detect and classify objects in an image based on locations. In this paper, we proposed a methodology to detect and classify the image's pixels' locations using enhanced bag of words (BOW). It calculates the initial positions of each segment of an image using superpixels and then ranks it according to the region score. Further, this information is used to extract local and global features using a hybrid approach of Scale Invariant Feature Transform (SIFT) and GIST, respectively. To enhance the classification accuracy, the feature fusion technique is applied to combine local and global features vectors through weight parameter. The support vector machine classifier is a supervised algorithm is used for classification in order to analyze the proposed methodology. The Pascal Visual Object Classes Challenge 2007 (VOC2007) dataset is used in the experiment to test the results. The proposed approach gave the results in high-quality class for independent objects’ locations with a mean average best overlap (MABO) of 0.833 at 1,500 locations resulting in a better detection rate. The results are compared with previous approaches and it is proved that it gave the better classification results for the non-rigid classes.
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source Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals
subjects Feature Extraction
Image Proposals
Object Classification
Object Detection
Segmentation
title A Hybrid Proposed Framework for Object Detection and Classification
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