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Object Detection Using Peak, Balanced Division Point and Shape Based Features

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Data Management, Analytics and Innovation (ICDMAI 2022)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 137))

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Abstract

In this paper, numerous techniques have been presented based upon the structure or geometrical shape of an object. By extracting the features of an object, we can detect and recognize an object. In this work, we firstly detect and count the number of objects available within an image. Each object is cropped and resized, and boundary values of an object are detected, which further helps extract the relevant features of an object. The various features extracted in this work are contiguous horizontal and vertical peak extent features, non-connected and connected contour segment features, vertical and horizontal balanced division point, chord features, etc. These features further assist in finding shape of a given object. For object detection and recognition of an object, the Linear-SVM and k-NN classifiers are used during classification. In this work, we have taken total 1020 images from MPEG dataset; these images include both, i.e. training and testing. The dataset consists of a total of 51 classes, and each class contains 20 images. In this, we achieve the accurateness of 91 and 90% by the use of Linear-SVM Classifier for object recognition using the proposed vertical and horizontal peak extent feature extraction methods.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Baba Farid College, Bathinda, India

    Shalu Gupta

  2. NIELIT, Guwahati, India

    Yumnam Jayanta Singh

Authors
  1. Shalu Gupta
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  2. Yumnam Jayanta Singh
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Corresponding author

Correspondence to Shalu Gupta .

Editor information

Editors and Affiliations

  1. Bangabasi Morning College, Kolkata, West Bengal, India

    Saptarsi Goswami

  2. Vara Technology, Saket, Delhi, India

    Inderjit Singh Barara

  3. Society for Data Science, Pune, Maharashtra, India

    Amol Goje

  4. National University of Singapore, Singapore, Singapore

    C. Mohan

  5. Department of Computer Science, Technion—Israel Institute of Technology, Haifa, Israel

    Alfred M. Bruckstein

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Gupta, S., Singh, Y.J. (2023). Object Detection Using Peak, Balanced Division Point and Shape Based Features. In: Goswami, S., Barara, I.S., Goje, A., Mohan, C., Bruckstein, A.M. (eds) Data Management, Analytics and Innovation. ICDMAI 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 137. Springer, Singapore. https://doi.org/10.1007/978-981-19-2600-6_2

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