深度学习LIFT：一种新型局部特征点方法

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"这篇资源主要涵盖了LIFT（Learned Invariant Feature Points）算法的论文和作者的个人注解，探讨了深度网络如何实现特征点检测、方向估计和特征描述的完整处理流程，并在不需重新训练的情况下在多个基准数据集上超越了现有的最优方法。关键词包括局部特征、特征描述符和深度学习。" LIFT（Learned Invariant Feature Transform）是一种创新的深度网络架构，旨在一次性解决计算机视觉中关键的局部特征问题：特征点的检测、方向估计以及特征描述。传统的局部特征方法如SIFT（尺度不变特征变换）和SURF（加速稳健特征）等，虽然在各自的领域表现出色，但LIFT通过深度学习将这三个步骤统一在一个可微分的框架下，实现了整体性能的提升。在论文中，作者KwangMoo Yi、Eduard Trulls、Vincent Lepetit和Pascal Fua展示了他们的深度学习模型如何能够有效地学习到不变性，这是在图像变换后保持特征识别能力的关键。他们指出，尽管之前的深度学习方法已经分别解决了这些子问题，但LIFT的独特之处在于它能够在保持端到端可微分的同时，将这些任务整合在一起。 LIFT的工作流程可能包括以下几个阶段： 1. **特征检测**：首先，网络会检测图像中的显著点，这些点通常是对图像变化鲁棒的局部特征。 2. **方向估计**：网络随后会计算每个特征点的主方向，这对于描述符的旋转不变性至关重要。 3. **特征描述**：最后，网络生成一个描述符向量，该向量能捕获特征点周围的图像信息，用于匹配目的。论文中提到，LIFT在多个基准测试集上表现优于现有的最佳方法，而无需对新数据进行再训练。这表明，LIFT具有很好的泛化能力和适应性，能够处理各种场景和图像条件下的特征匹配问题。此外，关键词"Local Features"强调了局部特征在图像识别和匹配中的核心地位，这些特征是许多计算机视觉任务的基础，如立体视觉、物体识别和图像拼接等。"Feature Descriptors"是表示这些局部特征的方式，好的描述符应具备不变性和区分性，使特征在不同的光照、尺度和角度下仍能被准确识别。"Deep Learning"则指出了利用深层神经网络来学习和优化这些特征处理任务的潜力。 LIFT的贡献在于提供了一个全面的深度学习解决方案，将传统的局部特征处理流程整合进一个单一的网络中，提高了效率和准确性，为计算机视觉领域开辟了新的研究方向。

4 K. M. Yi, E. Trulls, V. Lepetit, P. Fua

DET

Crop

DET

ORI

DESC

Crop

Rot

softargmax

✓

LEARNING

Fig. 2. Our Siamese training architecture with four branches, which takes as input

a quadruplet of patches: Patches P

and P

(blue) correspond to diﬀerent views of

the same physical point, and are used as positive examples to train the Descriptor;

(green) shows a diﬀerent 3D point, which serves as a negative example for the

Descriptor; and P

(red) contains no distinctive feature points and is only used as a

negative example to train the Detector. Given a patch P, the Detector, the softargmax,

and the Spatial Transformer layer Crop provide all together a smaller patch p inside P.

p is then fed to the Orientation Estimator, which along with the Spatial Transformer

layer Rot, provides the rotated patch p

that is processed by the Descriptor to obtain

the ﬁnal description vector d.

patches with CNNs trained on large volumes of data. For example, MatchNet [7]

trained a Siamese CNN for feature representation, followed by a fully-connected

network to learn the comparison metric. DeepCompare [8] showed that a net-

work that focuses on the center of the image can increase performance. The

approach of [27] relied on a similar architecture to obtain state-of-the-art results

for narrow-baseline stereo. In [10], hard negative mining was used to learn com-

pact descriptors that use on the Euclidean distance to measure similarity. The

algorithm of [28] relied on sample triplets to mine hard negatives.

In this work, we rely on the architecture of [10] because the corresponding

descriptors are trained and compared with the Euclidean distance, which has a

wider range of applicability than descriptors that require a learned metric.

3 Method

In this section, we ﬁrst formulate the entire feature detection and description

pipeline in terms of the Siamese architecture depicted by Fig. 2. Next, we discuss

the type of data we need to train our networks and how to collect it. We then

describe the training procedure in detail.

3.1 Problem formulation

We use image patches as input, rather than full images. This makes the learning

scalable without loss of information, as most image regions do not contain key-

points. The patches are extracted from the keypoints used by a SfM pipeline, as

will be discussed in Section 3.2. We take them to be small enough that we can

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深度学习LIFT：一种新型局部特征点方法

Mastering-Python-for-Finance + 打开量化交易的黑箱

LIFT: Learned Invariant Feature Transform

尺度不变特征变换匹配算法Scale Invariant Feature Transform （SIFT）

python 将{180576, 224919, 232683, 345594, 767055} -> {150501} (conf: 0.848, supp: 0.000, lift: 272.998, conv: 6.551) 转化为DataFrame

LIFT和 LoFTR 算法的区别

LIFT算法匹配出的特征点较Harris算法稀疏的原因

{180576, 224919, 232683, 345594, 767055} -> {150501} (conf: 0.848, supp: 0.000, lift: 272.998, conv: 6.551) 转化为DataFranme

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