Image Information Contribution Evaluation for Plant Diseases Classification via Inter-Class Similarity
نویسندگان
چکیده
Combineingplant diseases identification and deep learning algorithm can achieve cost-effective prevention effect, has been widely used. However, the current field of intelligent plant still faces problems insufficient data inaccurate classification. Aiming to resolve these problems, present research proposes an image information contribution evaluation method based on analysis inter-class similarity. Combining this with active selection strategy provide guidance for collection annotation datasets diseases, so as improve recognition effect reduce cost. The proposed includes two modules: inter-classes similarity module module. images located decision boundary between high classes will be images, they more In order verify effectiveness method, experiments were carried fine-grained classification dataset tomato diseases. Experimental results confirm superiority compared others. This is in disease For detection segmentation, further advisable.
منابع مشابه
Multi-label Classification for Image Annotation via Sparse Similarity Voting
We present a supervised multi-label classification method for automatic image annotation. Our method estimates the annotation labels for a test image by accumulating similarities between the test image and labeled training images. The similarities are measured on the basis of sparse representation of the test image by the training images, which avoids similarity votes for irrelevant classes. Be...
متن کاملClass-Aware Similarity Hashing for Data Classification
This paper introduces “class-aware similarity hashes” or “classprints,” which are an outgrowth of recent work on similarity hashing. The approach builds on the notion of context-based hashing to create a framework for identifying data types based on content and for building characteristic similarity hashes for individual data items that can be used for correlation. The principal benefits are th...
متن کاملInter-Cluster Features for Medical Image Classification
Feature encoding plays an important role for medical image classification. Intra-cluster features such as bag of visual words have been widely used for feature encoding, which are based on the statistical information within each clusters of local features and therefore fail to capture the inter-cluster statistics, such as how the visual words co-occur in images. This paper proposes a new method...
متن کاملImage Classification via Sparse Representation and Subspace Alignment
Image representation is a crucial problem in image processing where there exist many low-level representations of image, i.e., SIFT, HOG and so on. But there is a missing link across low-level and high-level semantic representations. In fact, traditional machine learning approaches, e.g., non-negative matrix factorization, sparse representation and principle component analysis are employed to d...
متن کاملDeep Unsupervised Domain Adaptation for Image Classification via Low Rank Representation Learning
Domain adaptation is a powerful technique given a wide amount of labeled data from similar attributes in different domains. In real-world applications, there is a huge number of data but almost more of them are unlabeled. It is effective in image classification where it is expensive and time-consuming to obtain adequate label data. We propose a novel method named DALRRL, which consists of deep ...
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: Sustainability
سال: 2022
ISSN: ['2071-1050']
DOI: https://doi.org/10.3390/su141710938