A knowledge enforcement network-based approach for classifying a photographer's images

Palaiahnakote, Shivakumara and Chowdhury, Pinaki Nath and Pal, Umapada and Doermann, David and Ramachandra, Raghavendra and Lu, Tong and Blumenstein, Michael (2022) A knowledge enforcement network-based approach for classifying a photographer's images. International Journal of Pattern Recognition and Artificial Intelligence, 36 (15). ISSN 0218-0014, DOI https://doi.org/10.1142/S021800142250046X.

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Classification of photos captured by different photographers is an important and challenging problem in knowledge-based and image processing. Monitoring and authenticating images uploaded on social media are essential, and verifying the source is one key piece of evidence. We present a novel framework for classifying photos of different photographers based on the combination of local features and deep learning models. The proposed work uses focused and defocused information in the input images to extract contextual information. The model estimates the weighted gradient and calculates entropy to strengthen context features. The focused and defocused information is fused to estimate cross-covariance and define a linear relationship between them. This relationship results in a feature matrix fed to Knowledge Enforcement Network (KEN) for obtaining representative features. Due to the strong discriminative ability of deep learning models, we employ the lightweight and accurate MobileNetV2. The output of KEN and MobileNetV2 is sent to a classifier for photographer classification. Experimental results of the proposed model on our dataset of 46 photographer classes (46234 images) and publicly available datasets of 41 photographer classes (218303 images) show that the method outperforms the existing techniques by 5%-10% on average. The dataset created for the experimental purpose will be made available upon publication.

Item Type: Article
Funders: Ministry of Education, Malaysia, Fundamental Research Grant Scheme (FRGS) (Grant No: FRGS/1/2020/ICT02/UM/02/4), National Natural Science Foundation of China (NSFC) (Grant No: 61672273), ISI-UTS Joint Research Cluster (TIH)
Uncontrolled Keywords: Focused region classification; Defocused region classification; Low-high pass filters; Entropy features; Deep learning; Photographer identification
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Computer Science & Information Technology > Department of Computer System & Technology
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 21 Nov 2023 04:48
Last Modified: 21 Nov 2023 04:48
URI: http://eprints.um.edu.my/id/eprint/40250

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