相较于一般物体的图像，农作物害虫图像因具有复杂的农田环境背景，分类与识别更加困难。为提高害虫图像识别的准确率，该文提出一种基于图像稀疏编码与空间金字塔模型相结合的害虫图像表示与识别方法。该方法利用大量非标注的自然图像块构造过完备学习字典，并运用该学习字典实现对害虫图像的多空间稀疏表示。与此同时，结合多核学习，该文设计了一种害虫图像识别算法。通过对35种害虫的识别，试验结果表明：在相同方法下，该文所提特征提取方法可使平均识别精度提高9.5百分点；此外，进一步通过对221种昆虫及20种蝴蝶的识别，试验结果表明：与传统方法相比较，该文所提方法使得平均识别精度提高14.1百分点。%Automatic classification of insect species in field crops such as corn, soybean, wheat, and canola is more difficult than the generic object classification because of complex background in filed and high appearance similarity among insect species. In this paper, we propose an insect recognition system on the basis of advanced sparse coding and spatial pyramid model. We firstly learn features from a large amount of unlabeled insect image patches to construct an over-complete dictionary. The sparse coding of insect image patches is obtained by encoding over the dictionary. To enhance discriminative ability of the sparse coding, we then apply multiple scales of filters coupled with different spaces. Finally, the multiple space features of sparse coding are seamlessly embedded into a multi-kernel framework for robust classification. Traditionally, insect recognition has mainly relied on manual identification by expert entomologists. However, for laymen without a thorough understanding of the terminology of insect taxonomy and morphological characteristics, it is hard to discriminate insect categories at the species level. Therefore, effective identification of insects is a key issue that needs to be well addressed. To improve the recognition accuracy, we develop an insect recognition system using advanced sparse coding, spatial pyramid model and multiple-kernel learning techniques. Different from traditional feature representation, a novel feature representation that is multiple-space sparse coding of insect objects is proposed by this work. The work flow of our method can be decomposed into 2 stages. The first stage focuses on image or insect object representation. At this stage, the features of insect images are extracted using advanced sparse coding and spatial pyramid model. The second stage, which deals with effective fusion of multiple insect-categorization features, constructs a kernel-level fusion classifier using all the sparse coding features. At the first stage, for an insect image given, the features of insect images are extracted firstly. The features are then represented as a linear combination of the corresponding training feature dictionary. Then, a multiple-space sparse coding with spatial pyramid model is used to represent insect image in a joint sparse way over all the features. In this process, for the object image, an over-complete dictionary with unlabeled insect images is learned first. Then, the local image patches of the insect object are represented by their sparse codes with the training dictionary. Despite the fact that appearance is modeled using local patches, the global structure information is necessary for accurate insect identification. Consequently, insect appearance is represented by concatenating the location and orientation sparse-coding of all image patches. To obtain the more compact representations of insect images, we use spatial pyramid model at multi-scale levels, which achieves better robustness to noise and clutter, and thus better copes with severe variations in the pose, scale or rotation. In this paper, we use the 3-scale level pyramid to represent insect image. At the top level of the pyramid, there are 4 image patches which represent the whole image. Each image block size is 50×50. The middle level contains 16 equal size non-overlapping image patches, for which each image block size is 25×25; and the bottom level has 64 image patches for which each image block size is 12×12. Then, the local image features of the 3-level pyramid are combined to represent the insect appearance. The larger scale level provides better geometric features when the classifying insects undergo large appearance variations, while smaller scale level obtains finer features. Finally,the features from fine to coarse levels across different scales are concatenated together to generate the final feature representation of insect images for insect classification.At the second stage, the multiple-kernel learning approach is adopted to combine multiple-space sparse coding. As different features of insect images contribute differently to the classification of insect species, the multiple-space sparse coding technique can combine multiple features of insect species to enhance the recognition performance. Given positive and negative insect samples, the features are extracted. Local image patches of the samples are then represented by multiple-space sparse coding using the corresponding training dictionary. Finally, the multiple-kernel learning classifier is constructed by learning the multiple-space sparse coding of the negative and positive samples for insect categorization and recognition. To meet the need of practical insect image identification, we collected insect images covering various species across several common field crops including corn, soybean, wheat, and canola. Samples of 35 common pest species found in field crops were collected, such asPieris rapae (Linnaeus), andLeptocorisa acuta(Thunberg). Experimental results showed that our proposed method performed well on the classification of insect species, and outperformed the state-of-the-art methods of the generic insect recognition. Our method improved the recognition rate by more than 9% compared to other methods for the same data sets. In addition, the proposed method had also a good performance and enhanced the average recognition accuracy by 14.1% for the different data sets.
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