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Visual words of Bag-of-Visual-Words (BoVW) framework are independent each other, which results in not only discarding spatial orders between visual words but also lacking semantic information. This study is inspired by word embeddings that a similar embedding procedure is applied to a large number of visual words. By this way, the corresponding embedding vectors of the visual words can be formulated...
In recent years, deep convolutional neural networks have achieved state of the art performance in various computer vision tasks such as classification, detection or segmentation. Due to their outstanding performance, CNNs are more and more used in the field of document image analysis as well. In this work, we present a CNN architecture that is trained with the recently proposed PHOC representation...
The H-KWS 2016, organized in the context of the ICFHR 2016 conference aims at setting up an evaluation framework for benchmarking handwritten keyword spotting (KWS) examining both the Query by Example (QbE) and the Query by String (QbS) approaches. Both KWS approaches were hosted into two different tracks, which in turn were split into two distinct challenges, namely, a segmentation-based and a segmentation-free...
Autonomous monitoring of fruit crops based on mobile camera sensors requires methods to segment fruit regions from the background in images. Previous methods based on color and shape cues have been successful in some cases, but the detection of textured green fruits among green plant material remains a challenging problem. A recently proposed method uses sparse keypoint detection, keypoint descriptor...
In this paper we present a method for the segmentation of continuous page streams into multipage documents and the simultaneous classification of the resulting documents. We first present an approach to combine the multiple pages of a document into a single feature vector that represents the whole document. Despite its simplicity and low computational cost, the proposed representation yields results...
Every year, new sensor technologies are being implemented to improve the acquisition ofhigh-resolution remote sensing images (RSIs). With the large amount of data provided by these sensors,novel computational approaches are constantly required to supportthe decision-making process based on RSI analysis. A typical problem is therecognition of target regions for land-cover mapping. In this context,...
Automatic object annotation usually requires complicated segmentation to separate foreground objects from the background scene. However, the statistical content of a background scene can in fact provide resourceful valuable information for image retrieval. In this paper, we propose a new hybrid kernel that incorporates local features extracted from both dense regular grids and interest points for...
Scale invariance is a desirable property for many vision tasks such as image segmentation and classification. One way to achieve such invariance is to collect images containing objects of all scales and then train a classifie r. In practice, however, only a finite number of images at a finite number of scales can be collected, and this poses the problem of scale sampling. In this paper, we focus on...
This article shows the improvement of automatic cartoon classification. Two new visual features - color component and color kind based on region segmentation - are proposed. Compared to traditional HSV color histogram and texture, experiment using the two new features can achieve better result, with less dimensions and higher mining efficiency.
This paper presents a novel method for learning classes of temporal sequences using a bag-of-features approach. We define a temporal sequence as a bag of temporal features and show how this representation can be used for the recognition and segmentation of temporal events. A codebook of temporal descriptors, representing the local temporal texture, is automatically constructed from a set of sample...
We propose semantic texton forests, efficient and powerful new low-level features. These are ensembles of decision trees that act directly on image pixels, and therefore do not need the expensive computation of filter-bank responses or local descriptors. They are extremely fast to both train and test, especially compared with k-means clustering and nearest-neighbor assignment of feature descriptors...
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