A Hybrid Proposed Framework for Object Detection and Classification

Muhammad Aamir, Yi-Fei Pu, Ziaur Rahman, Waheed Ahmed Abro, Hamad Naeem, Farhan Ullah, Aymen Mudheher Badr, Journal of Information Processing Systems Vol. 14, No. 5, pp. 1176-1194, Oct. 2018

https://doi.org/10.3745/JIPS.02.0095
Keywords: Image Proposals, feature extraction, Object Classification, Object Detection, Segmentation
Fulltext:

Abstract

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.

Statistics

Show / Hide Statistics

Statistics (Cumulative Counts from November 1st, 2017)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.

Cite this article

[APA Style]

Aamir, M., Pu, Y., Rahman, Z., Abro, W., Naeem, H., Ullah, F., & Badr, A. (2018). A Hybrid Proposed Framework for Object Detection and Classification. Journal of Information Processing Systems, 14(5), 1176-1194. DOI: 10.3745/JIPS.02.0095.

[IEEE Style]

M. Aamir, Y. Pu, Z. Rahman, W. A. Abro, H. Naeem, F. Ullah, A. M. Badr, "A Hybrid Proposed Framework for Object Detection and Classification," Journal of Information Processing Systems, vol. 14, no. 5, pp. 1176-1194, 2018. DOI: 10.3745/JIPS.02.0095.

[ACM Style]

Muhammad Aamir, Yi-Fei Pu, Ziaur Rahman, Waheed Ahmed Abro, Hamad Naeem, Farhan Ullah, and Aymen Mudheher Badr. 2018. A Hybrid Proposed Framework for Object Detection and Classification. Journal of Information Processing Systems, 14, 5, (2018), 1176-1194. DOI: 10.3745/JIPS.02.0095.