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Softmax Transition Probabilities

Convert frame similarities to transition probabilities using softmax.

For varied video texture playback, we use probabilistic transitions instead of always jumping to the best match. The transition probability from frame $i$ to frame $j$ is:

$P(j|i) = \frac{\exp(-D_{ij}/\sigma)}{\sum_k \exp(-D_{ik}/\sigma)}$

where:

$D_{ij}$ is the dissimilarity from $i$ to $j$
$\sigma$ is a temperature parameter
Lower dissimilarity = higher probability

This creates smooth random playback that prefers good transitions but allows variety.

Example:

Input:

transition_probs([0, 10, 10], 10)

Output:

[0.5761, 0.2119, 0.2119]

Reasoning:

Converting similarities [0, 10, 10] with σ=10: exp(-0/10) = 1.0 exp(-10/10) = 0.3679 exp(-10/10) = 0.3679

sum = 1.7358
Probs = [1.0/1.7358, 0.3679/1.7358, 0.3679/1.7358] ≈ [0.576, 0.212, 0.212]

Constraints:

similarities: list of similarity scores from current frame to all frames
sigma: temperature parameter (higher = more uniform)
Return probability distribution (sums to 1)

More from CV: Image-Based Rendering

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Background Knowledge

Video Textures and Frame Transitions

Video textures are short video clips that can be seamlessly looped or extended by intelligently stitching frames together. Rather than simply repeating a video or always jumping to the most similar frame, the key insight is to use probabilistic transitions—randomly selecting the next frame with probabilities weighted by how well it matches the current frame. This creates natural-looking, varied playback that avoids repetitive patterns while maintaining temporal coherence.

Softmax and Temperature Scaling

The softmax function converts raw similarity scores into a valid probability distribution. In this context, the dissimilarity scores $D_{ij}$ are first converted to similarities using the exponential function, then normalized so all probabilities sum to 1. The temperature parameter $\sigma$ controls how "sharp" or "smooth" the probability distribution is: a small $\sigma$ makes the distribution peaky (favoring the best matches strongly), while a large $\sigma$ flattens it (allowing more exploration of suboptimal transitions). This is crucial for balancing quality (preferring good transitions) with variety (allowing occasional less-optimal jumps).

Numerical Stability Considerations

When implementing softmax with exponentials, you'll encounter numerical challenges. Computing $\exp(-D_{ij}/$ \sigma $)$ directly can cause overflow (when values are very negative) or underflow (when values are very positive). The standard solution is the log-sum-exp trick: subtract the maximum dissimilarity value before computing exponentials, which doesn't change the final probabilities but keeps intermediate values in a safe numerical range.

Algorithm/Approach

The general pattern is:

Normalize dissimilarities by subtracting the minimum (or maximum) value for numerical stability
Apply exponential transformation with temperature scaling: $\exp(-D_{ij}/$ \sigma $)$
Compute the partition function (denominator): sum all exponential values
Normalize by dividing each exponential by the partition function to get probabilities

This transforms a raw distance metric into a proper probability distribution suitable for random sampling.

Step-by-Step Strategy

Step 1: Understand Your Input

You'll have a dissimilarity matrix $D$ where $D[i][j]$ represents how different frame $i$ is from frame $j$
You'll have a temperature parameter $\sigma$ (typically a small positive value like 0.1 or 1.0)

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solution.py

Python 3.13

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