High-order graph matching kernel for early carcinoma EUS image classification
نویسندگان
چکیده
منابع مشابه
Local Coding Based Matching Kernel Method for Image Classification
This paper mainly focuses on how to effectively and efficiently measure visual similarity for local feature based representation. Among existing methods, metrics based on Bag of Visual Word (BoV) techniques are efficient and conceptually simple, at the expense of effectiveness. By contrast, kernel based metrics are more effective, but at the cost of greater computational complexity and increase...
متن کاملCPM: A Graph Pattern Matching Kernel with Diffusion for Accurate Graph Classification
Graph data mining is an active research area. Graphs are general modeling tools to organize information from heterogenous sources and have been applied in many scientific, engineering, and business fields. With the fast accumulation of graph data, building highly accurate predictive models for graph data emerges as a new challenge that has not been fully explored in the data mining community. I...
متن کاملAdaptive Kernel Matching Pursuit for Pattern Classification
1 A sparse classifier is guaranteed to generalize better than a denser one, given they perform identical on the training set. However, methods like Support Vector Machine, even if they produce relatively sparse models, are known to scale linearly as the number of training examples increases. A recent proposed method, the Kernel Matching Pursuit, presents a number of advantages over the SVM, lik...
متن کاملSpatial Graph for Image Classification
Spatial information in images is considered to be of great importance in the process of object recognition. Recent studies show that human’s classification accuracy might drop dramatically if the spatial information of an image is removed. The original bag-of-words (BoW) model is actually a system simulating such a classification process with incomplete information. To handle the spatial inform...
متن کاملHistogram intersection kernel for image classification
In this paper we address the problem of classifying images, by exploiting global features that describe color and illumination properties, and by using the statistical learning paradigm. The contribution of this paper is twofold. First, we show that histogram intersection has the required mathematical properties to be used as a kernel function for Support Vector Machines (SVMs). Second, we give...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Multimedia Tools and Applications
سال: 2015
ISSN: 1380-7501,1573-7721
DOI: 10.1007/s11042-015-3108-1