A Robust Co-saliency Object Detection Model by Applying CLAHE and Otsu Segmentation Method
Keywords:
CLAHE, Cosal2015 Dataset, COCO dataset, Otsu segmentation, Object Detection, Thin ResNetAbstract
The co-saliency detection technique is utilized in several applications namely image retrieval, picture annotation, as well as surveillance, among others. Its purpose is to identify the most significant and comparable patterns from a relevant group of images. In this study, there has been proposed a novel approach to co-saliency detection that utilizes the modified ResNet 50 model as a classification model. The contrast-limited adaptive histogram equalization (CLAHE) along with Otsu segmentation techniques were used as pre-processing steps to better identify and isolate prominent objects in the image. These techniques helped the model recognize patterns more efficiently. The Thin ResNet model was fine-tuned and optimized for improved accuracy, and the SGDM optimizer was used for network compilation. For training and testing of the model, we used the Cosal2015 and COCO datasets, with a 70:30 ratio. The proposed model demonstrated superior performance metrics such as MAE as well as F-measure score in comparison to state-of-the-art techniques. This proposed model obtains a high F1-score of 98.6% along with MAE of 0.034 on widely known Cosal2015 dataset. Also, this model obtained F1-score and MAE scores 94.2%, and 0.164, respectively on the COCO dataset.
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