Discriminative feature representations and heterogeneous fusion for plant leaf recognition

Abstract

Effective feature representation and heterogeneous fusion are essential for plant leaf recognition. However, existing methods have several limitations, such as insufficient comprehensiveness and distinctiveness in feature representation, as well as a lack of full consideration for the compatibility and complementarity in heterogeneous fusion. In the end, we propose a discriminative shape representation named the bag of multiscale curvature angle cuts (BMCAC) to capture fine curvature and spatial distribution characteristics, an advanced deep representation called the progressive salient deep representation (PSDR) to fully exploit deep convolutional features, and an effective fusion framework termed the K-weighted shape and deep feature fusion (KWFF) to aggregate the local context and global importance of heterogeneous features. Specifically, BMCAC is derived from the curvature angle cuts (CAC), multiscale analysis, and the bag of visual words (BoVW) model; PSDR is constructed by applying progressive downsampling and hierarchical pooling operations to deep convolutional features; and KWFF is developed by encoding neighboring information using homogeneous distance measures while incorporating globally weighted contributions from heterogeneous distance measures. Extensive experiments on four well-known benchmark leaf datasets demonstrate that the proposed shape and deep representations can efficiently extract leaf image features, and the fusion framework can effectively integrate heterogeneous features, outperforming state-of-the-art methods. The source code is available at https://github.com/Mumuxi1123/BMCAC_PSDR_KWFF.

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