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2013 2nd IAPR Asian Conference on Pattern Recognition最新文献

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Haze Removal from Single Images Based on a Luminance Reference Model 基于亮度参考模型的单幅图像去雾
2013 2nd IAPR Asian Conference on Pattern Recognition Pub Date : 2013-11-05 DOI: 10.1109/ACPR.2013.119
Jiafeng Li, Hong Zhang, Ding Yuan, Helong Wang
{"title":"Haze Removal from Single Images Based on a Luminance Reference Model","authors":"Jiafeng Li, Hong Zhang, Ding Yuan, Helong Wang","doi":"10.1109/ACPR.2013.119","DOIUrl":"https://doi.org/10.1109/ACPR.2013.119","url":null,"abstract":"Optical transmission estimation is a key procedure for removing haze from certain outdoor images. In this paper, we propose a novel transmission estimation model called the luminance reference model. A luminance reference, which is the intensity lower bound of a local region in the haze free image, is assumed to be a global constant across the image. Based on this assumption, we theoretically prove that, with an appropriate luminance reference, the transmission can be estimated accurately. By using a scene-dependent estimate of the luminance reference, our method can be applied to different types of images. We further propose a two-step guided approach to rapid and robust computation of a transmission map. Our experimental results show that the proposed method is computationally efficient, while producing comparable visual results to the existing state-of-the-art, but more complex methods.","PeriodicalId":365633,"journal":{"name":"2013 2nd IAPR Asian Conference on Pattern Recognition","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131881773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 6
Visual-Based Image Retrieval by Block Reallocation Considering Object Region 考虑目标区域的块重新分配视觉图像检索
2013 2nd IAPR Asian Conference on Pattern Recognition Pub Date : 2013-11-05 DOI: 10.1109/ACPR.2013.106
T. Mochizuki, H. Sumiyoshi, Masanori Sano, Mahito Fujii
{"title":"Visual-Based Image Retrieval by Block Reallocation Considering Object Region","authors":"T. Mochizuki, H. Sumiyoshi, Masanori Sano, Mahito Fujii","doi":"10.1109/ACPR.2013.106","DOIUrl":"https://doi.org/10.1109/ACPR.2013.106","url":null,"abstract":"Visual-based image retrieval based on the visual similarity over the entire image is very useful when targeting various kinds of large-volume content. This method generally divides an image into grid-shaped blocks and uses similarities based on a comparison of image features between corresponding block regions in two different images. However, the method sometimes fails in terms of object-conscious retrieval when their backgrounds are almost the same but the only object is different or object's positions and/or sizes are different. In this paper, we propose a new method featuring the reallocation of some blocks into the object region (OB-blocks) and the new similarity score with placing weight on the OB-blocks, which are derived from visual saliency map. Our proposed method could realize the \"visual-based and object-conscious\" image retrieval. We verified the effectiveness of this method through comparison experiments.","PeriodicalId":365633,"journal":{"name":"2013 2nd IAPR Asian Conference on Pattern Recognition","volume":"34 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116479164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Bag-of-Visual-Phrases via Local Contexts 通过本地语境的视觉短语袋
2013 2nd IAPR Asian Conference on Pattern Recognition Pub Date : 2013-11-05 DOI: 10.1109/ACPR.2013.158
E. Román-Rangel, S. Marchand-Maillet
{"title":"Bag-of-Visual-Phrases via Local Contexts","authors":"E. Román-Rangel, S. Marchand-Maillet","doi":"10.1109/ACPR.2013.158","DOIUrl":"https://doi.org/10.1109/ACPR.2013.158","url":null,"abstract":"This paper extends the bag-of-visual-words representations to a bag-of-visual-phrases model. The introduced bag-of-visual-phrases representation is constructed upon a proposed method for probabilistic description of co-occurring visual words, which is adapted for each reference word. This bag-of-visual-phrases representation implicitly encodes spatial relationships among visual words, thus being a richer representation while remaining as compact as the bag-of-visual-words model. We demonstrate the effectiveness of our method with a series of statistical analysis and retrieval experiments, and show that it largely outperforms previous methods for construction of bag representations. Furthermore, our method allows to query traditional bag-of-words vs the proposed bag-of-phrases. We conducted retrieval experiments on a dataset of complex shapes, whose instances correspond to hieroglyphs of the pre-Columbian Maya culture from the ancient Americas.","PeriodicalId":365633,"journal":{"name":"2013 2nd IAPR Asian Conference on Pattern Recognition","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123641904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Living without Menu Bar: A Shape Retrieval Based Word Editor 没有菜单栏的生活:一个基于形状检索的文字编辑器
2013 2nd IAPR Asian Conference on Pattern Recognition Pub Date : 2013-11-05 DOI: 10.1109/ACPR.2013.194
Xiang Ruan, Kenji Fukuda, Takayoshi Yamashita
{"title":"Living without Menu Bar: A Shape Retrieval Based Word Editor","authors":"Xiang Ruan, Kenji Fukuda, Takayoshi Yamashita","doi":"10.1109/ACPR.2013.194","DOIUrl":"https://doi.org/10.1109/ACPR.2013.194","url":null,"abstract":"Shape descriptor plays very important role in shape retrieval system especially in the case of input shapes are drawn by hand. A good descriptor should be not only deformation tolerant but also compact and less memory consuming. With this in mind, we propose a new shape descriptor by which features are extracted at salient locations of the shape and then encoded using a vocabulary tree. To intuitively show performance of the proposed algorithm, we build a word editor demo program. Unlike other word editors, our program let users search pictures, draw auto-shapes or send instructions all by drawing a \"shape\" without using menu bar.","PeriodicalId":365633,"journal":{"name":"2013 2nd IAPR Asian Conference on Pattern Recognition","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123669659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Label-Related/Unrelated Topic Switching Model: A Partially Labeled Topic Model Handling Infinite Label-Unrelated Topics 标签相关/不相关主题切换模型:处理无限标签无关主题的部分标记主题模型
2013 2nd IAPR Asian Conference on Pattern Recognition Pub Date : 2013-11-05 DOI: 10.1109/ACPR.2013.163
Yasutoshi Ida, Takuma Nakamura, Takashi Matsumoto
{"title":"Label-Related/Unrelated Topic Switching Model: A Partially Labeled Topic Model Handling Infinite Label-Unrelated Topics","authors":"Yasutoshi Ida, Takuma Nakamura, Takashi Matsumoto","doi":"10.1109/ACPR.2013.163","DOIUrl":"https://doi.org/10.1109/ACPR.2013.163","url":null,"abstract":"We propose a Label-Related/Unrelated Topic Switching Model (LRU-TSM) based on Latent Dirichlet Allocation (LDA) for modeling a labeled corpus. In this model, each word is allocated to a label-related topic or a label-unrelated topic. Label-related topics utilize label information, and label-unrelated topics utilize the framework of Bayesian Nonparametrics, which can estimate the number of topics in posterior distributions. Our model handles label-related and -unrelated topics explicitly, in contrast to the earlier model, and improves the performances of applications to which is applied. Using real-world datasets, we show that our model outperforms the earlier model in terms of perplexity and efficiency for label prediction tasks that involve predicting labels for documents or pictures without labels.","PeriodicalId":365633,"journal":{"name":"2013 2nd IAPR Asian Conference on Pattern Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129744439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Object Recognition by Combining Binary Local Invariant Features and Color Histogram 二值局部不变特征与颜色直方图相结合的目标识别
2013 2nd IAPR Asian Conference on Pattern Recognition Pub Date : 2013-11-05 DOI: 10.1109/ACPR.2013.103
Dung Phan, Chi-Min Oh, Soohyung Kim, In Seop Na, Chil-Woo Lee
{"title":"Object Recognition by Combining Binary Local Invariant Features and Color Histogram","authors":"Dung Phan, Chi-Min Oh, Soohyung Kim, In Seop Na, Chil-Woo Lee","doi":"10.1109/ACPR.2013.103","DOIUrl":"https://doi.org/10.1109/ACPR.2013.103","url":null,"abstract":"In this paper, we propose an approach for object recognition using binary local invariant features and color information. In our approach, we use a fast detector for key point detection and binary local features descriptor for key point description. For local feature matching, the Fast library for Approximated Nearest Neighbors (FLANN) is applied to match the query image and reference image in data set. A homography matrix which represents transformation of object in scene image and reference image is estimated from matching pairs by using the Optimized Random Sample Consensus Algorithm (ORSA). Then, we detect object location in the image, and remove background of image. Next, significant color feature is used to calculate global color histogram since it reflects main content of primitive image and also ignores noises. Similarity of query image and reference object image is a linear combination of color histogram correlation and number of feature matches. As a result, the proposed method can overcome drawbacks of object recognition method using only local features or global features. In addition, the use of binary feature makes feature description as well as feature matching faster to meet the requirement of a real time system. For evaluation, we experiment with two well-known and latest local invariant features including the Oriented Fast and Rotated Binary Robust Independent Elementary Features (ORB) and Fast Retina Key point (FREAK) and a planar object data set. According to the result, ORB feature shows that it is powerful as our system obtained the higher accuracy and fast processing time. The experimental results also proved that combination of binary local invariant feature and significant color is effective for planar object recognition.","PeriodicalId":365633,"journal":{"name":"2013 2nd IAPR Asian Conference on Pattern Recognition","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128213845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 8
Random Decomposition Forests 随机分解森林
2013 2nd IAPR Asian Conference on Pattern Recognition Pub Date : 2013-11-05 DOI: 10.1109/ACPR.2013.97
Chun-Han Chien, Hwann-Tzong Chen
{"title":"Random Decomposition Forests","authors":"Chun-Han Chien, Hwann-Tzong Chen","doi":"10.1109/ACPR.2013.97","DOIUrl":"https://doi.org/10.1109/ACPR.2013.97","url":null,"abstract":"We present an effective image representation based on a new tree-structured coding technique called `random decomposition forests' (RDFs). Our method combines the merits of visual-word representations and random forests. The proposed RDF is able to decompose a local descriptor into multiple sets of visual words in a recursive and randomized manner. We show that, when combined with standard multiscale and spatial pooling strategies, the code vectors generated by RDF yield a powerful representation for image categorization. We are able to achieve state-of-the-art performance on several popular benchmark datasets.","PeriodicalId":365633,"journal":{"name":"2013 2nd IAPR Asian Conference on Pattern Recognition","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129313476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
An Analysis of Properties of Malignant Cases for Imbalanced Breast Thermogram Feature Classification 乳腺热成像特征分类不平衡恶性病例的特点分析
2013 2nd IAPR Asian Conference on Pattern Recognition Pub Date : 2013-11-05 DOI: 10.1109/ACPR.2013.45
B. Krawczyk, G. Schaefer
{"title":"An Analysis of Properties of Malignant Cases for Imbalanced Breast Thermogram Feature Classification","authors":"B. Krawczyk, G. Schaefer","doi":"10.1109/ACPR.2013.45","DOIUrl":"https://doi.org/10.1109/ACPR.2013.45","url":null,"abstract":"Medical thermography has been demonstrated an effective and inexpensive method for detecting breast cancer, in particular for tumors in early stages and in dense tissue. Image features can be extracted from breast thermograms and used in a pattern classification stage for automated diagnosis and hence as a second objective opinion or for screening purposes. One of the main challenges for applying machine learning algorithms to this task is the high imbalance ratio between class distributions in the available training data. In this paper, we carefully examine the properties of the malignant minority class in order to gain insight into the nature of the data. We identify different types of minority class samples present in a breast thermogram dataset comprising about 150 cases. Using the gained knowledge, we analyse the performance of three state-of-the-art ensemble classifiers, a cost-sensitive one, one based on over-sampling and one using under-sampling, to evaluate which objects are the most difficult to classify correctly. Experimental analysis shows that there is a strong correlation between the type of minority sample and the performance of specific classifier ensemble types.","PeriodicalId":365633,"journal":{"name":"2013 2nd IAPR Asian Conference on Pattern Recognition","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128958097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Automatic Compensation of Radial Distortion by Minimizing Entropy of Histogram of Oriented Gradients 基于梯度方向直方图熵最小化的径向畸变自动补偿
2013 2nd IAPR Asian Conference on Pattern Recognition Pub Date : 2013-11-05 DOI: 10.1109/ACPR.2013.167
Yuta Kanuki, N. Ohta, A. Nagai
{"title":"Automatic Compensation of Radial Distortion by Minimizing Entropy of Histogram of Oriented Gradients","authors":"Yuta Kanuki, N. Ohta, A. Nagai","doi":"10.1109/ACPR.2013.167","DOIUrl":"https://doi.org/10.1109/ACPR.2013.167","url":null,"abstract":"A car-mounted camera for driver's assistance has a wide angle view, but at the same time, it also has a serious radial distortion. This paper presents a method which can automatically estimate the distortion parameters without using any specially-made patterns for calibration. Our method uses the fact that we are surrounded by many artificial objects consisted of straight lines, e.g., buildings, signboards, and telephone poles, when we are driving. Although these straight lines become curved lines on the camera image because of the distortion, it is easily expected that the appropriately compensated image has the most straight lines. In order to quantify the amount of straight lines, we introduce the entropy of Histogram of Oriented Gradients (HOG) over the whole image. The entropy of HOG is expected to become minimum when the image has the most straight lines. Using this property, the distortion parameters are estimated. The experimental results show that the estimated distortion parameters generate appropriately undistorted images.","PeriodicalId":365633,"journal":{"name":"2013 2nd IAPR Asian Conference on Pattern Recognition","volume":"43 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114006545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Kinect Depth Inpainting via Graph Laplacian with TV21 Regularization 基于TV21正则化的Kinect深度绘图
2013 2nd IAPR Asian Conference on Pattern Recognition Pub Date : 2013-11-05 DOI: 10.1109/ACPR.2013.35
Shaoguo Liu, Ying Wang, Haibo Wang, Chunhong Pan
{"title":"Kinect Depth Inpainting via Graph Laplacian with TV21 Regularization","authors":"Shaoguo Liu, Ying Wang, Haibo Wang, Chunhong Pan","doi":"10.1109/ACPR.2013.35","DOIUrl":"https://doi.org/10.1109/ACPR.2013.35","url":null,"abstract":"Depth maps provided by Microsoft Kinect often contain large dark holes around depth boundaries and occasional missing pixels in non-occluded regions, as well as noise, which prevent their further usage in real-world applications. In this paper, we present a graph Laplacian based framework to restore missing pixels based on the strong correlation between color image and depth map. To preserve sharp edges and remove noise, the TV21 (Total Variation) prior of depth maps is then integrated as an additional regularizer to the framework. Finally, an efficient and effective iterative optimization method with a closed-form solution at each iteration is presented to address this issue. Experiments conducted on both real scene images and synthetic images demonstrate that our approach gives better performance than commonly-used depth in painting schemes.","PeriodicalId":365633,"journal":{"name":"2013 2nd IAPR Asian Conference on Pattern Recognition","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131937411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 6
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