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SAI 2018: Intelligent Computing pp 497–510Cite as

Object Recognition Using SVM Based Bag of Combined Features

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 858))

Abstract

The evolving and task-dependent nature of visual categories of problems prompts for an example based solutions involving machine learning approach. One such technique, ‘Bag of features’ approach has gained popularity in computer vision applications, including texture recognition, image classification and robot localization. Despite being quite newer, system based on bag of feature method has set performance standards on benchmark of image classification and has achieved drastic scalability in image retrieval. In this paper, a novel approach is being presented for object recognition using combined bag of features and multiclass Support Vector Machine (SVM). Proposed approach presents SVM based combined bag of features method and emphasizes on better recognition and more classification accuracy from images dataset. Although nearest neighbor classifiers have been employed in this area previously, but they suffer from high variance problem in case of limited sampling. Root-SIFT and SURF descriptors are combined for the construction of combined set of bag features, which has reasonable computational complexity yielding excellent results. A Caltech dataset; considered to be very challenging database because of objects are embedded in clutter background having different poses and scales; has been used for testing efficacy of approach. Comparison, made among state-of-the-art approaches shows promising result. Our experiments show state-of-the-art performance on benchmark dataset for object recognition. On Caltech dataset, we achieved a correct classification rate of 65 and 72 at 15 and 30 training images.

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Author information

Authors and Affiliations

  1. National University of Sciences and Technology, Islamabad, Pakistan

    Fozia Mehboob & Muhammad Abbas

  2. Department of Computer Science, Al-Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Kingdom of Saudi Arabia

    Abdul Rauf

Authors
  1. Fozia Mehboob
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  2. Muhammad Abbas
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  3. Abdul Rauf
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Corresponding author

Correspondence to Fozia Mehboob .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Department of Information Science, Saga University, Honjo, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

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Mehboob, F., Abbas, M., Rauf, A. (2019). Object Recognition Using SVM Based Bag of Combined Features. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Computing. SAI 2018. Advances in Intelligent Systems and Computing, vol 858. Springer, Cham. https://doi.org/10.1007/978-3-030-01174-1_37

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