A new image classification technique using tree-structured regional features
نویسندگان
چکیده
Image classification is a challenging problem of computer vision. Conventional image classification methods use flat image features with fixed dimensions, which are extracted from a whole image. Such features are computationally effective but are crude representation of the image content. This paper proposes a new image classification approach through a tree-structured feature set. In this approach, the image content is organized in a two-level tree, where the root node at the top level represents the whole image and the child nodes at the bottom level represent the homogeneous regions of the image. The tree-structured representation combines both the global and the local features through the root and the child nodes. The tree-structured feature data are then processed by a two-level self-organizing map (SOM), which consists of an unsupervised SOM for processing image regions and a supervising concurrent SOM (CSOM) classifier for the overall classification of images. The proposed method incorporates both global image features and local region-based features to improve the performance of image classification. Experimental results show that this approach performs better than conventional approaches. r 2006 Elsevier B.V. All rights reserved.
منابع مشابه
Classification of emotional speech using spectral pattern features
Speech Emotion Recognition (SER) is a new and challenging research area with a wide range of applications in man-machine interactions. The aim of a SER system is to recognize human emotion by analyzing the acoustics of speech sound. In this study, we propose Spectral Pattern features (SPs) and Harmonic Energy features (HEs) for emotion recognition. These features extracted from the spectrogram ...
متن کاملObject-Based Classification of UltraCamD Imagery for Identification of Tree Species in the Mixed Planted Forest
This study is a contribution to assess the high resolution digital aerial imagery for semi-automatic analysis of tree species identification. To maximize the benefit of such data, the object-based classification was conducted in a mixed forest plantation. Two subsets of an UltraCam D image were geometrically corrected using aero-triangulation method. Some appropriate transformations were perfor...
متن کاملAutomatic road crack detection and classification using image processing techniques, machine learning and integrated models in urban areas: A novel image binarization technique
The quality of the road pavement has always been one of the major concerns for governments around the world. Cracks in the asphalt are one of the most common road tensions that generally threaten the safety of roads and highways. In recent years, automated inspection methods such as image and video processing have been considered due to the high cost and error of manual metho...
متن کاملImproved Generative Models for Continuous Image Features through Tree-structured Non-parametric Distributions
Density estimation arises in a wide range of vision problems and methods which can deal with high dimensional image features are of great importance. While in principle a nonparametric distribution can be estimated for the full feature distribution using Parzen windows technique, the amount of data to make these estimates accurate is usually either unattainable or unmanageable. Consequently, mo...
متن کاملClassification of encrypted traffic for applications based on statistical features
Traffic classification plays an important role in many aspects of network management such as identifying type of the transferred data, detection of malware applications, applying policies to restrict network accesses and so on. Basic methods in this field were using some obvious traffic features like port number and protocol type to classify the traffic type. However, recent changes in applicat...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Neurocomputing
دوره 70 شماره
صفحات -
تاریخ انتشار 2007