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Kotenseki is a collection of classical and ancient Japanese literature. It is comprised of image books that express Japanese stories by using comic drawings of different characters, such as humans, nature, and animals. To effectively store them for posterity, a search system is important. We propose an efficient CBIR system to assist the users in easily accessing the information and have an enjoyable...
The aim of saliency object detection algorithms is to find objects in image or video which draw attention of humans at the first sight. This very popular topic in robotics and computer vision research is useful in various areas and applications like object segmentation, adaptive compression, object recognition, visual surveillance and so on. In this paper, we will explore the possibilities of using...
Given a query image containing the object of interest (OOI), we propose a novel learning framework for retrieving relevant frames from the input video sequence. While techniques based on object matching have been applied to solve this task, their performance would be typically limited due to the lack of capabilities in handling variations in visual appearances of the OOI across video frames. Our proposed...
In this paper, we propose a novel selective search method to speed up the object detection via category-based attention scheme. The proposed attentional searching strategy is designed to focus on a small set of selected regions where the object category is expected to exist. The selected regions are estimated by mimicking three properties of the attentional scheme of human visual perception: spotlighting...
A knowledge on spatial relationships between objects present in a given collection of images can provide interesting information to improve classical CBIR tasks such as object detection and localization, by reducing the searching areas of the object relatively to one or several given objects. In this paper, we propose a representation of the knowledge on relationships existing between symbolic objects...
In this paper, we present a biological inspired coarse-to-fine approach to detect pedestrians in a given image. In the coarse detection step, a probable pedestrian area is predicted with both global and local features. Global orientation features are extracted to infer the spatial configuration of an image scene. Specifically, the vertical location of the pedestrian area is retrieved based on the...
Accuracy in image object detection has been usually achieved at the expense of much computational load. Therefore a trade-off between detection performance and fast execution commonly represents the ultimate goal of an object detector in real life applications. In this present work, we propose a novel method toward that goal. The proposed method was grounded on a multi-scale spectral residual (MSR)...
Assessing and selecting relevant visual cues is crucial for rapid saliency estimation and visual search. Here, we derive a new optimal feature modulation strategy to maximize the relative salience of the target, in which the top-down weight on a feature map depends on its stimulation intensity ratio (SIR) between the target and the distractors. The stimulation intensity is determined by two factors,...
This paper proposes a model for trail detection that builds upon the observation that trails are salient structures in the robot's visual field. Due to the complexity of natural environments, the straightforward application of bottom-up visual saliency models is not sufficiently robust to predict the location of trails. As for other detection tasks, robustness can be increased by modulating the saliency...
Object localization in an image is usually handled by searching for an optimal subwindow that tightly covers the object of interest. However, the subwindows considered in previous work are limited to rectangles or other specified, simple shapes. With such specified shapes, no subwindow can cover the object of interest tightly. As a result, the desired subwindow around the object of interest may not...
State-of-the-art object retrieval systems are mostly based on the bag-of-visual-words representation which encodes local appearance information of an image in a feature vector. A search is performed by comparing query object's feature vector with those for database images. However, a database image vector generally carries mixed information of an entire image which may contain multiple objects and...
Many object detection systems rely on linear classifiers embedded in a sliding-window scheme. Such exhaustive search involves massive computation. Efficient Subwindow Search (ESS) avoids this by means of branch and bound. However, ESS makes an unfavourable memory tradeoff. Memory usage scales with both image size and overall object model size. This risks becoming prohibitive in a multiclass system...
This paper describes a new video event detection framework based on subspace selection technique. With the approach, feature vectors presenting different kinds of video information can be easily projected from different modalities onto an unified subspace, on which recognition process can be performed. The approach is capable of discriminating different classes and preserving the intra-modal geometry...
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