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The problem of fine-grained object recognition is very challenging due to the subtle visual differences between different object categories. In this paper, we propose a task-driven progressive part localization (TPPL) approach for fine-grained object recognition. Most existing methods follow a two-step approach that first detects salient object parts to suppress the interference from background scenes...
This paper introduces self-taught object localization, a novel approach that leverages deep convolutional networks trained for whole-image recognition to localize objects in images without additional human supervision, i.e., without using any ground-truth bounding boxes for training. The key idea is to analyze the change in the recognition scores when artificially masking out different regions of...
Local image features around interest-points have been widely used in order to exploit the similarities between different views of an object in different images. While there are numerous algorithms on detecting the interest-points and defining the local features, few have focused on the importance of the matching process. In this paper, we presented a method that matches interest-points detected via...
This paper presents object-based image retrieval using a novel method based on perceptual grouping. The perceptual grouping is obtained by detecting the line edge from a square block using the two consecutive primitive edge differences detector. Object segmentation and recognition is the primary step of computer vision for applying for an image retrieval of higher-level image analysis. However, automatic...
Periodicity is at the core of the recognition of many actions. This paper takes the following steps to detect and measure periodicity. 1) We establish a conceptual framework of classifying periodicity in 10 essential cases, the most important of which are flashing (of a traffic light), pulsing (of an anemone), swinging (of wings), spinning (of a swimmer), turning (of a conductor), shuttling (of a...
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