66 research outputs found
Spectral salient object detection
© 2014 IEEE. Many existing methods for salient object detection are performed by over-segmenting images into non-overlapping regions, which facilitate local/global color statistics for saliency computation. In this paper, we propose a new approach: spectral salient object detection, which is benefited from selected attributes of normalized cut, enabling better retaining of holistic salient objects as comparing to conventionally employed pre-segmentation techniques. The proposed saliency detection method recursively bi-partitions regions that render the lowest cut cost in each iteration, resulting in binary spanning tree structure. Each segmented region is then evaluated under criterion that fit Gestalt laws and statistical prior. Final result is obtained by integrating multiple intermediate saliency maps. Experimental results on three benchmark datasets demonstrate the effectiveness of the proposed method against 13 state-of-the-art approaches to salient object detection
Improvised Salient Object Detection and Manipulation
In case of salient subject recognition, computer algorithms have been heavily
relied on scanning of images from top-left to bottom-right systematically and
apply brute-force when attempting to locate objects of interest. Thus, the
process turns out to be quite time consuming. Here a novel approach and a
simple solution to the above problem is discussed. In this paper, we implement
an approach to object manipulation and detection through segmentation map,
which would help to desaturate or, in other words, wash out the background of
the image. Evaluation for the performance is carried out using the Jaccard
index against the well-known Ground-truth target box technique.Comment: 7 page
Image Cropping with Composition and Saliency Aware Aesthetic Score Map
Aesthetic image cropping is a practical but challenging task which aims at
finding the best crops with the highest aesthetic quality in an image.
Recently, many deep learning methods have been proposed to address this
problem, but they did not reveal the intrinsic mechanism of aesthetic
evaluation. In this paper, we propose an interpretable image cropping model to
unveil the mystery. For each image, we use a fully convolutional network to
produce an aesthetic score map, which is shared among all candidate crops
during crop-level aesthetic evaluation. Then, we require the aesthetic score
map to be both composition-aware and saliency-aware. In particular, the same
region is assigned with different aesthetic scores based on its relative
positions in different crops. Moreover, a visually salient region is supposed
to have more sensitive aesthetic scores so that our network can learn to place
salient objects at more proper positions. Such an aesthetic score map can be
used to localize aesthetically important regions in an image, which sheds light
on the composition rules learned by our model. We show the competitive
performance of our model in the image cropping task on several benchmark
datasets, and also demonstrate its generality in real-world applications.Comment: Accepted by AAAI 2
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