Abstract
Object localization is a fundamental and important task in computer vision, that is used as a pre-processing step for object detection and semantic segmentation. However, fully supervised object localization requires bounding boxes and pixel-level labels, and these annotations are expensive. For this reason, Weakly Supervised Object Localization (WSOL) with image-level (weak) supervision has been the focus of much research in recent years. However, WSOL requires a large dataset to detect the region of an object in images with high performance. When the large dataset is unavailable, it is difficult to localize the image with high performance. This paper proposes a method for extracting target regions using small amounts of target and background images with image-level labels. The proposed method enables the detection of object locations with high performance using relatively less training images by classifying multiple patches cut from the image. This object localization method differs from the typical WSOL method that takes a single image as input and detects the location of an object because it assumes a small patch of area as input. The label of the patch cropped from the image must be labeled with the ground truth. However, the proposed method uses labels attached to images because ground truth labeling is costly. Instead, in the proposed method, the network learns by learning many “background” labeled background patches, and learns to induce the network to classify the mislabeled background patches that resemble ground truth as background. We call this key idea Decision-Boundary Induction(DBI). Moreover, learning many background patches for such a DBI is what we call background learning. In our experiments, we verified that decision boundaries are induced, and accordingly, we could roughly extract the target region. Also, we showed that the Loc. Acc. is higher than that of WSOL.
Prof. Masaru Tanaka deceased in June 2021.
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Acknowledgments
I am grateful to Associate Professor Takafumi Amaha of Fukuoka University for advice on writing the paper. I would like to take this opportunity to thank him.
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Nakamura, R., Ueda, Y., Tanaka, M., Fujiki, J. (2023). Rough Target Region Extraction with Background Learning. In: Na, I., Irie, G. (eds) Frontiers of Computer Vision. IW-FCV 2023. Communications in Computer and Information Science, vol 1857. Springer, Singapore. https://doi.org/10.1007/978-981-99-4914-4_3
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