Affiliations: [a]
Department of Information and Communication Engineering, Anna University, Chennai, India
| [b]
Department of Information Technology, Panimalar Engineering College, Chennai, India
| [c]
Department of Computer Science & Engineering, Panimalar Engineering College City Campus, Chennai, India
| [d]
Department of Electronics and Communication Engineering, Panimalar Engineering College, Chennai, India
Correspondence:
[*]
Corresponding author. K. Lalitha, Research Scholar, Department of Information and Communication Engineering, Anna University and Associate Professor, Department of Information Technology, Panimalar Engineering College, Chennai, 600025, India. E-mail:[email protected].
Abstract: For retrieving the relevant images from the internet, CBIRs (content based image retrievals) techniques are most globally utilized. However, the traditional image retrieval techniques are unable to represent the image features semantically. The CNNs (convolutional neural networks) and DL has made the retrieval task simpler. But, it is not adequate to consider only the finalized aspect vectors from the completely linked layers to fill the semantic gap. In order to alleviate this problem, a novel Hash Based Feature Descriptors (HBFD) method is proposed. In this method, the most significant feature vectors from each block are considered. To reduce the number of descriptors, pyramid pooling is used. To improve the performance in huge databases, the hash code like function is introduced in each block to represent the descriptors. The proposed method has been evaluated in Oxford 5k, Paris 6k, and UKBench datasets with the accuracy level of 80.6%, 83.9% and 92.14% respectively and demonstrated better recall value than the existing methods.
Keywords: Content-based image retrieval, CNNs, hash based feature descriptor (HBFD), pyramid pooling and hash code
