Over the past three weeks, I’ve been consulting on a data science project for AptDeco.com. AptDeco is a peer-to-peer online marketplace for buying and selling used furniture. The website simplifies the resale process by handling all of the logistics for its users. The AptDeco team fills in any missing details about the furniture, creates high quality listings on their website, and even delivers the furniture when it’s purchased.

The first step of creating a listing on AptDeco is to submit a picture of the furniture. The editors at AptDeco will review the pictures, and decide if they are of high enough quality to be edited and displayed on the front page of the listing. Unfortunately, 75% of the submitted images are low quality, and can’t be displayed on the front of a listing. This means that AptDeco’s team spends a large amount of time sifting through low quality pictures and requesting improvements from their users. In addition to the amount of time this takes, some users never submit better images, leading to a lost sale for AptDeco and its users.

With this in mind, I created DecoRater — an algorithm for automatically assessing the quality of furniture images on AptDeco.com. DecoRater provides immediate feedback to AptDeco users, increasing the odds that they will submit high quality images for their listings. DecoRater can also be used behind the scenes, to reduce the number of low-quality photos that the AptDeco team has to sift through, and to flag listings which are in high need of editor intervention.

In this post, I’ll explain how I created DecoRater from the ground up. The discussion will include:

Choosing an appropriate model

Before I describe how to assess the quality of each image, we need to define what the “quality” of an image means. AptDeco’s database has a number of metrics which we could use to define “quality.” Most of these metrics, such as a listing’s click through rate and conversion rate, are influenced by outside factors, such as the price of a listing, or the age of the furniture. Even if we account for these factors, the images that are displayed in the store are already edited. This means that any model which uses these metrics could only suggest improvements to the already-edited images, rather than to the raw images that users upload directly.

A more useful measure of image quality can be defined by AptDeco’s choice to edit and display an image, or to reject it. AptDeco’s editors don’t record the outcome of this decision in their database, but they were willing to help me hand-label a subset of 2000 images for this purpose. In this light, the goal of assessing image quality becomes a binary classification problem in which we try to predict the probability that an unedited picture will be edited and displayed on the front page of a listing.

To accomplish this goal, we’ll need to train a machine learning model to emulate a human’s subjective opinion of each image’s quality. The go-to model for image processing is a convolutional neural network (CNN), which requires minimal preprocessing of an image, and can easily account for the spatial correlation of individual pixels. Unfortunately, we lack the large amount of labeled data that is required to train a CNN.

Instead, we’ll need to implement a simpler classification model. After considering multiple algorithms, I settled on an AdaBoost Random Forest Classifier due to its higher test performance relative to the other models. The random forest model well suited for the nonlinearity of the classification problem, and the AdaBoost boosting algorithm helps to properly classify the many borderline images in our database. Most importantly, the model can output the probability that an image belongs to the “high quality” class, rather than simply classifying the images on a binary scale. This is a nice feature to have, since we can use the continuous probability to rank the quality of images within the “high quality” and “low quality” groups. (Note: This is why support vector machines were not considered, since the output of these models should not be interpreted as a continuous probability.)

Extracting Different Color Spaces From an Image

The input features for our model will need to describe different characteristics of each image. I’ll detail each of these features in the next section, but suffice it to say that many of them will describe how colors change throughout the picture. We can represent these colors in a number of different color spaces, which each have their own advantages. In this section, I’ll discuss the four color spaces that we’ll be working with.

1) The RGB Color Space

The RGB (Red,Green,Blue) color space describes each color as a fraction of red, green, and blue light. The intensity of each component determines the final color, with zero intensity from each channel resulting in black, and full intensity from each channel resulting in white. This color space is useful for measuring the brightness of images, as well as the gradients of the red, green, and blue color channels across the image.

2) The HSV Color Space

The HSV (Hue,Saturation,Value) color space is a cylindrical representation of the RGB color model that attempts to be more perceptually relevant than the standard RGB space. Within the cylinder, the angle around the vertical axis represents the “hue” of the color, which denotes the wavelength of light which is most dominant. Since hue is measured as an angle around a cylinder, we’ll need to use circular statistics when quantifying it. The distance from the center of the cylinder represents the “saturation” of the color, which measures the colorfulness or purity of a color relative to how bright it is. Mixing a completely saturated color with white lowers the saturation and causes the color to appear white-washed. Finally, the depth of the cylinder represents the “value” of the color, which measures the brightness of the color. A low value corresponds to a low brightness. The HSV color space is useful for measuring how white-washed or dark an image appears, and for measuring how complementary certain colors appear to the human eye.

3) The LAB Color Space

The LAB (Lightness, a, b) color space represents colors using two color-opponent dimensions (a and b) and the overall lightness of the image. The A-opponent axis ranges from green to red, while the B-opponent axis ranged from yellow to blue. The LAB color space is particularly useful for measuring how colorful an image appears, since a wider area in the A-B plane corresponds to a wider range of colors in the image.

4) The Grayscale Channel

The grayscale channel of an image encodes color intensity with varying shades of gray. The grayscale channel varies from black, at zero intensity, to white, at full intensity. This channel is useful for calculating non-color related features, such as the blurriness of an image, or the location of focal points.

Extracting Image Features

Now that we have a number of color spaces to work with, we’re ready to extract features from our images. We’ll start with the low hanging fruit before moving on to more complex features.

1) Image size – 2 features

All of the pictures in our database begin in a 1500x1500 pixel format, but many of them contain white borders that would not be displayed in the online listings. After we remove these borders, the images take on a wide range of shapes and sizes. We can measure the total number of pixels in the cropped images, as well as the aspect ratio (height-to-width ratio) to produce two features that tell us about the shape of a picture.

From left to right: An image with an aspect ratio of 0.51, an aspect ratio of 1.0, and an aspect ratio of 1.81

2) Average Color Intensity – 6 features

Although a user won’t have much control over the color of their pictures, the overall color of the image may nonetheless impact its aesthetic quality. We can easily measure the average intensity of each color by working with the individual channels of the RGB or HSV color spaces. For each channel, we calculate the average intensity over the image and use the result to create six more features.

Top: An image with high intensity in the blue color channel
Bottom: An image with low intensity in the blue color channel

3) Variation of Color Intensity – 6 features

We can also measure the variation of intensity within each color channel. A low standard deviation of the intensity in channel may indicate that the image is tinted that color, while a high standard deviation may indicate bright lights or shadows in the image. Calculating the standard deviation for both the RGB and HSV color spaces provides six additional image features.

Top: An image with low variation in the blue color channel.
Bottom: An image with high variation the blue color channel.

4) Color Gradients – 12 features

In calculus, the “gradient” of a function defines how quickly that function changes with respect to a particular variable. A function which changes quickly with respect to a variable will have a high gradient, while a function which changes slowly with respect to a variable will have a low gradient. We can use this concept to measure how quickly each color changes throughout our image. The gradient is measured using a Sobel Filter, which removes noise from neighboring pixels before calculating the difference of intensity between them. We can calculate the gradient of the RGB and HSV filters, in both the horizontal and vertical directions, providing 12 additional image features. These features are particularly useful for detecting shadows or glares in the picture, which will present themselves as high gradients due to the quickly varying shades of light.

It is worth noting that the “color gradient” and “variation of color intensity” features are similar, but measure two different quantities. Specifically, the gradient features take correlation of nearby pixels into account, while the standard deviation is applied to the image as a whole.

Top: An image with low horizontal gradients in the blue color channel
Bottom: An image with high horizontal gradient in the blue color channel, indicating the presence of shadows.

5) Colorfulness of an image – 1 feature

The LAB color space is particularly useful when measuring how “colorful” an image is. The A and B channels represent opposing colors, so the total area of an image in the A-B plane corresponds to the colorfulness of an image. Hasler and Suesstrunk present a formula for quantifying the perceived colorfulness of an image based on their 2003 experimental study. The formula is given by:

where the first two terms are measuring the spread of the image along the A and B axes, and the third term is a slight modification that relates to the average color of the picture.

Top: Low colorfulness (left) and high colorfulness (right) images.
Bottom: The two images projected on the A-B plane. The colorful image (green) occupies more area than the colorless image (red).

6) Complementary Colors – 1 feature

Complementary colors are located directly across from each other on the color hue circle, as shown in the figure below. We can use complex analysis to measure the overall amount of complementary colors in an image. Specifically, the equation

defines a circle of radius one in the complex plane, where the real coordinate of a number is given by , and the imaginary part is given by . By imagining the circle in the complex plane as the color hue circle, we can use the real and imaginary coordinates of each color to measure how complementary they are.

If you are unfamiliar with complex analysis, this will be a hard concept to grasp. It may be helpful to think of the problem in term of the real component alone — that is, in terms of . If we have one color that is located at an angle of zero on the hue circle, and a complementary color located on the other side of the circle at , then the cosine of these angles will equal and , respectively. With this scheme, the sum of two complementary colors would equal zero.

It’s more intuitive to assign a high value to complementary colors, which we can achieve by multiplying the hue angle by a factor of two. In our previous example, the colorfulness values would become , and , allowing the sum of complementary colors to add together. This transformation also allows uncomplementary colors to cancel out. For instance, a color at and a color at on the hue circle would have values of and respectively.

Using the method described above, we can measure the total amount of complementary colors in an image by calculating for each pixel of an image. Next, we add the norm of the resulting numbers together, and divide by the total number of pixels in the image:

Complementary Color Index

The result is a number between zero and one, with one representing an image made of perfectly complementary images (such as red and green), and zero representing an image made of perfectly uncomplementary images (such as red and blue).

An illustration of complementary colors on the Hue wheel.

7) Darkness of an image – 3 features

The easiest way to measure the “brightness” of an image is to make a histogram of the intensity values within the R,G, and B channels. Since values that are closer to zero represent black, and values that are closer to 255 represent white, we can take the average value of the intensity histogram as a measure of how dark the image is.

A more nuanced way to measure the “brightness” of an image is to use the average relative luminance of the image, which emphasizes the physiological aspects of the human eye. Relative luminance is defined by

We can also use the average perceived luminance of the image, which accounts for psychological aspects of human perception, and is given by:

Note that all three of these image features originate from the relative intensities of the RGB channels, so they will be highly correlated.

Top: Low luminosity (left) and high luminosity (right) images.
Bottom: A histogram of the pixel intensity for the two images. The bright image (green) has a right-leaning histogram, while the dark image (red) has a left-leaning histogram.

8) Blurriness of an image – 5 features

I have taken two separate approaches to measure how blurry an image is. The first approach applies a Laplacian Filter to the image, which calculates the second derivative of a single channel from the image. If you aren’t familiar with calculus, this means the laplacian filter calculates how quickly color changes accelerate in the image. Typically, edges that are present in the image will have a high value in the laplacian filter. If the variance of the laplacian filter is high, then the image has a wide range of edge-like and non-edge-like responses. On the other hand, if the variance of the laplacian is low, then there are very few edges present in the picture — likely indicating a blurry image. Since the laplacian filter is applied to a single channel, we can measure the blurriness of the HSV and grayscale channels separately, producing 4 additional features.

A more complicated way to measure the blurriness of an image is to compute the Fast Fourier Transform of the image. Without going into too much detail, the Fourier Transform decomposes the image into high frequency and low frequency variations. High frequency variations indicate rapid changes in the image, which correspond to edge-like features. Therefore, the average frequency found in the Fourier Transform provides a measure of how blurry an image is, with higher frequencies corresponding to sharper images.

Top: A blurry image and the corresponding Laplacian filter.
Bottom: A sharp image and the corresponding Laplacian filter. Note the higher intensity of edges in the sharper image.

9) Focal points of an image – 3 features

We can identify focal points of an image by constructing a saliency map of the image. The concept of saliency originates from neuroscience, and measures the extent to which it stands out relative to its surroundings. The method we use to create a saliency map is described by Itti, Koch, and Niebur in their 1998 paper A model of Saliency-based Visual Attention for Rapid Scene Analysis. This method takes a bottom-up approach, in which the image is decomposed into 12 color maps, 6 intensity maps, and 24 orientation maps. The extent to which an object stands out within each map is calculated, and normalized saliency maps representing each of the three categories (color, intensity, and orientation) are produced. The final saliency map is created by averaging over the three normalized maps. Once we have identified areas of high saliency in our image, we can calculate the average hue, saturation, and value at these locations to produce three additional image features.

An image and the corresponding saliency map. White areas correspond to higher saliency, and identify focal points of the image.

10) Rule of Thirds – 5 features

The rule of thirds is a common guideline which applies to the composition of photographs. The rule states that an image should be divided into nine parts, with two horizontal lines and two vertical lines equally dividing the image into three rows and three columns. Proponents of the rule of thirds believe that aligning the subject of a photo with the intersection of these lines produces a more interesting composition than simply centering the subject.

In our case, AptDeco’s editors strongly prefer front-facing pictures of furniture, since they are easier to edit and display on the front page. Nonetheless, we can use the rule of thirds to measure just how centered a piece of furniture is in the image. We begin by drawing the lines of thirds on an image. After locating the intersection of these lines, we create a mask to select pixels which are close to the intersections, as shown in the figure below.

Once we have identified the rule of thirds intersections, we can calculate the average HSV and saliency at these locations, producing 4 additional image features. We can also calculate the distance between the highest saliency point and any of the intersections to determine how close the focal point is to one of the rule of thirds.

An image with the rule of thirds lines indicated in red. The shaded region near the intersections indicate the mask that is used to calculate feature near the intersections.

11) Image Symmetry – 16 Features

Measuring the symmetry of an image is accomplished by decomposing the image into symmetric () and antisymmetric () components. The symmetric component of an image is produced by mirroring the image across the axis of symmetry, and adding it to the original image. For instance, if we are measuring the horizontal symmetry of an image, we mirror the image across the vertical axis to produce the symmetric component. Similarly, the antisymmetric component of an image is produced by mirroring the image across the axis of symmetry and subtracting it from the original image. The total symmetry of an image is calculated by taking the ratio of the total intensity of the symmetric component to the total intensity of both components:

We can apply the symmetry calculation to the full HSV and saliency channels of an image, as well as the HSV and saliency channels near the rule of thirds alone. This produces an additional 16 image features for our model.

From left to right: The original image, the value channel of the image, and the corresponding antisymmetric and symmetric component of the value channel.

12) Number of Objects in an Image – 2 Features

Our last two features assess how “busy” an image appears. To accomplish this, we automatically threshold the image using Otsu’s Method to produce a black and white picture. Next, we draw contours around connected black pixels to identify individual objects within the picture. This process works extremely well for background subtracted images, but can unintentionally divide single objects into multiple contours when the thresholding process is not straightforward. To resolve this issue, we also keep track of the standard deviation of the centers of each contour. A low standard deviation indicates that many of the contours were found in the same location, and that the individual contours most likely originate from a single object.

From left to right: An image, the thresholded version of that image, and the original image with identified contours shown in green.

Training an Image Quality Model

Once we’ve extracted the features from each image, we’re ready to train our machine learning model. As I mentioned previously, we’ll being using an AdaBoost random forest classifier. I’ll provide a brief overview of the algorithm before discussing how well our model performs.

The foundation of a random forest classifier is called a decision tree. A decision tree splits our data into subsets according to the value of a specific feature. The goal of a decision tree is to end up with “pure” subsets of data, which contain only “high quality” or only “low quality” images. If a particular feature splits the data into a pure subset, no additional splitting is required. If the feature leaves a subset with a mix of “high quality” and “low quality” images, then the subset is split further by using another feature.

As an example, suppose we have the dataset shown below.

Each row of the table represents a particular furniture image. The first column specifies the quality of the image, and the remaining columns represent three different features that we could use to split our data. For clarity, I’ve color coded the rows according to the quality of the image.

For now, we will choose a random feature to create our decision tree. Suppose we split our data on the symmetry of an image. After we do so, we’ll end up with three subsets — low symmetry images, average symmetry images, and high symmetry images. From the table above, we can see that all of the average symmetry images are also high quality images, so we won’t need to split that subset any further. On the other hand, the low symmetry subset and high symmetry subset contain a mix of high and low quality images, so they will need to be split further. Up to this point, our decision tree looks like this:

We’ll need to split the low symmetry and high symmetry subsets further. By inspection, if we split the low symmetry subset using the darkness of the image, and the high symmetry subset using the blurriness of the image, then we’ll end up with completely pure subsets of data. Following this procedure, the final decision tree is depicted below:

In the example above, we chose our initial splitting features randomly. In reality, we should pick features that will improve the purity of each subsequent subset by the highest amount. If the feature we select produces a 50/50 mix of high quality and low quality images, that splitting was useless. In contrast, if the feature we select produces a pure subset, then the splitting is ideal. Mathematically, the process of choosing the best features to split on involves the calculation of the gini or entropy of a subset, and before calculating which splitting maximizes the information gain of our data.

Since a single decision tree tries to create the optimal splitting at the current depth of the tree, it’s possible that the final splittings are not the optimal splittings for the decision tree as a whole. This means that small changes in our training set can drastically change the structure of our decision tree. To resolve this problem, we create multiple decision trees. We allow each tree to choose a random subset of our features at each splitting to ensure the unique growth of each tree, and then have the “forest” of trees vote on the final outcome. This method is what we refer to as the Random Forest Classifier.

The fraction of trees which voted for a particular class can be interpreted as the probability that the image belongs to that class. We can also choose a probability threshold at which we classify the image as belonging to a particular class. Typically, a threshold of 50% is chosen so that the image is classified according to a majority vote.

So where does AdaBoost come in? In short, AdaBoost is a boosting algorithm which trains our model over multiple iterations. With each iteration, samples which were misclassified are given an increased weight in the algorithm. This forces the model to focus on hard to classify samples, such as images that are on the border of being high quality or low quality.

Assessing Model Performance

A common way to assess the performance of a binary classification algorithm is called the receiver operating characteristic curve (more commonly called the ROC curve). The ROC curve illustrates the classifier’s performance as the threshold for calling our picture “high quality” is altered. For example, suppose we classified everything with a “high quality” probability higher than 70% as a “high quality” image. In this case, the classifier requires the vast majority of trees to agree that the image is “high quality” before classifying it as such. With such a strict threshold, we’re likely to misclassify many of the high quality images as a low quality image. In exchange, we are less likely to misclassify a low quality image as a high quality image. In this situation, we say that our classifier has a low true positive rate (the fraction of high quality images that we correctly identified as high quality), and a low false positive rate (the fraction of low quality images that we incorrectly identified as high quality). As we lower the threshold of our classifier, we will increase both the true positive and false positive rate, tracing out the ROC curve. The ROC for our image classifier is shown below:

The dashed diagonal line in the figure above indicates how well our classifier would perform with completely random guesses. The most ideal classifier would have an extremely high true positive rate while maintaining a low false positive rate. Thus, higher quality classifiers will hug the top left corner of the ROC plot. As the ROC curve approaches the top left corner, the area under the curve (AUC) approached 1.0. In our case, our classifier has an AUC of 0.74, halfway between random guessing and perfect classification.

Another tool to evaluate our classifier is called a confusion matrix. The confusion matrix is a heat map which plots the true class of an image against the predicted class of an image, as shown below.

From the top row of the confusion matrix, we can see that 68% of the low quality images were correctly identified, and 32% of the low quality images were incorrectly identified. From the bottom row of the confusion matrix, we can see that 73% of the high quality images were correctly identified, and 27% of the high quality images were incorrectly identified. This information is also encoded by the color of the squares. (Note: The numbers presented here use a probability threshold of 50%)

The confusion matrix is particularly useful for evaluating classification models when the balance of classes is uneven. In our case, only about 25% of AptDeco’s images truly belong to the high quality class. Therefore, if our classifier always predicted that images were low quality, it would be correct 75% of the time. This would lead to a confusion matrix with dark squares in the first column, and light squares in the second column. In contrast, a high quality classifier will correctly identify both the majority and minority class, leading to dark squares in the top left and bottom right corners, as is the case with our classifier.

Implementing the Model

There are a few ways in which AptDeco can implement our model. First, we can use the model to identify the most common mistakes users make when uploading images, and to provide general guidelines to AptDeco users. We do so by identifying the most important features in our model (those which provided the most efficient splittings in our trees). A list of the top 15 features is shown below. We can see that the most important features have to do with the horizontal symmetry of brightness and saturation in the image. The location of the furniture with respect to focal points in the image, the presence of color gradients and shadows, and the perceived luminance of the image are also distinguishing factors.

AptDeco could also integrate our classifier with their current image upload system. The classifier would extract the relevant image features and calculate the probability that an image is high quality or not. If all of the user’s images a classified as low quality images, the website can immediately ask the user to upload a high quality picture. Note that the classifier incorrectly identifies 27% of high quality images when a probability threshold of 50% is used as the cut off. To avoid a frustrating user experience where we misclassify high quality images too often, it may be beneficial to lower the threshold for calling an image high quality in this case.

Finally, our classifier could be implemented in the backend of AptDeco’s website. In this situation, the classifier would predict the probability that each image is high quality, and store that information in a database. The editors at AptDeco could use this information to automatically identify which pictures are suitable for editing, and which listings are in urgent need of intervention. If the classifier is integrated into the backend of AptDeco, we don’t need to worry about a poor user experience, and can leave the probability threshold at 50%. Based on the performance metrics shown in the previous section, using the algorithm in this way would reduce the time that editors spend sifting through low quality images by 40%.