Vidit Kumar^1,*, Hemant Petwal², Ajay Krishan Gairola¹, Pareshwar Prasad Barmola¹

Computer Systems Science and Engineering, Vol.46, No.3, pp. 2711-2724, 2023, DOI:10.32604/csse.2023.032047

Abstract Fine-grained image search is one of the most challenging tasks in computer vision that aims to retrieve similar images at the fine-grained level for a given query image. The key objective is to learn discriminative fine-grained features by training deep models such that similar images are clustered, and dissimilar images are separated in the low embedding space. Previous works primarily focused on defining local structure loss functions like triplet loss, pairwise loss, etc. However, training via these approaches takes a long training time, and they have poor accuracy. Additionally, representations learned through it tend to tighten up in the embedded… More >

Sathit Prasomphan^*

Computer Systems Science and Engineering, Vol.44, No.2, pp. 1295-1307, 2023, DOI:10.32604/csse.2023.025293

Abstract Fine-grained image classification is a challenging research topic because of the high degree of similarity among categories and the high degree of dissimilarity for a specific category caused by different poses and scales. A cultural heritage image is one of the fine-grained images because each image has the same similarity in most cases. Using the classification technique, distinguishing cultural heritage architecture may be difficult. This study proposes a cultural heritage content retrieval method using adaptive deep learning for fine-grained image retrieval. The key contribution of this research was the creation of a retrieval model that could handle incremental streams of… More >