*Two different image search engines developed with Deep Learning algorithms*

# Introduction

Imagine that you want to search for similar images to any picture. Imagine searching on the web for similar images to the one we are taking with our phones. In this post, we are going to develop and compare two different ways in which using Deep Learning algorithms we can solve this problem of querying between thousands of images, the most similar images.

We are going to compare two different approaches:

- Autoencoder
- Image Features Extraction

# Data

We are going to solve this problem using the Flipkart images dataset. So we are going to find similar images from the products of this huge Indian e-commerce.

After downloading the images from the available URLs found on the data, we get 18322 images of different products.

The code for downloading images and developing both approaches is found on this Github repo.

# Autoencoder

Let’s try to get similar images, by using an Autoencoder model. This type of model consists of three main parts:

- Encoder
- Latent Space
- Decoder

The idea behind this model, is to reconstruct the input we feed the algorithm, so the input and output size is the same. So if we can the input, we can reduce the dimension of the image, to a very small vector, and this vector is the Latent Space. If the model is robust, we can reduce all the complexity of the image to a small dimension.

To train the Autoencoder, we are going to use the Keras module inside the Tensorflow 2.0 library. Once we have downloaded the images, we can define the training and validation set. Here, we are going to use the *ImageDataGenerator API.*

from tensorflow.keras.preprocessing.image import ImageDataGenerator, load_img, img_to_array, array_to_img from tensorflow.keras.models import Model, load_model from tensorflow.keras.layers import Flatten, Conv2D, Conv2DTranspose, LeakyReLU, BatchNormalization, Input, Dense, Reshape, Activation from tensorflow.keras.optimizers import Adam from tensorflow.keras.callbacks import ModelCheckpoint import tensorflow.keras.backend as K import numpy as np import matplotlib.pyplot as plt from tqdm import tqdm_notebook as tqdm import pickle import pandas as pd # Load images img_height = 256 img_width = 256 channels = 3 batch_size = 16 train_datagen = ImageDataGenerator(rescale=1./255, validation_split=0.2) training_set = train_datagen.flow_from_directory( './flipkart/images', target_size = (img_height, img_width), batch_size = batch_size, class_mode = 'input', subset = 'training', shuffle=True) validation_set = train_datagen.flow_from_directory( './flipkart/images', target_size = (img_height, img_width), batch_size = batch_size, class_mode = 'input', subset = 'validation', shuffle=False)

The argument `class_mode='input'`

is the key here. On the ImageDataGenerator documentation, we found the following:

**class_mode**: one of “binary”, “categorical”, “input”, “multi_output”, “raw”, sparse” or None. Default: “categorical”. Mode for yielding the targets: —`"binary"`

: 1D numpy array of binary labels, –`"categorical"`

: 2D numpy array of one-hot encoded labels. Supports multi-label output. –`"input"`

: images identical to input images (mainly used to work with autoencoders), –`"multi_output"`

: list with the values of the different columns, –`"raw"`

: numpy array of values in`y_col`

column(s), –`"sparse"`

: 1D numpy array of integer labels, –`None`

, no targets are returned (the generator will only yield batches of image data, which is useful to use in`model.predict()`

).

Also, for this to work, you should have all images inside another folder, so the Keras API assumes you have only one class. In this example, I have the following images directory: `flipkart/images/images/...`

Now, we can define our model architecture, and fit it with the images:

# Define the autoencoder input_model = Input(shape=(img_height, img_width, channels)) # Encoder layers encoder = Conv2D(32, (3,3), padding='same', kernel_initializer='normal')(input_model) encoder = LeakyReLU()(encoder) encoder = BatchNormalization(axis=-1)(encoder) encoder = Conv2D(64, (3,3), padding='same', kernel_initializer='normal')(encoder) encoder = LeakyReLU()(encoder) encoder = BatchNormalization(axis=-1)(encoder) encoder = Conv2D(64, (3,3), padding='same', kernel_initializer='normal')(input_model) encoder = LeakyReLU()(encoder) encoder = BatchNormalization(axis=-1)(encoder) encoder_dim = K.int_shape(encoder) encoder = Flatten()(encoder) # Latent Space latent_space = Dense(16, name='latent_space')(encoder) # Decoder Layers decoder = Dense(np.prod(encoder_dim[1:]))(latent_space) decoder = Reshape((encoder_dim[1], encoder_dim[2], encoder_dim[3]))(decoder) decoder = Conv2DTranspose(64, (3,3), padding='same', kernel_initializer='normal')(decoder) decoder = LeakyReLU()(decoder) decoder = BatchNormalization(axis=-1)(decoder) decoder = Conv2DTranspose(64, (3,3), padding='same', kernel_initializer='normal')(decoder) decoder = LeakyReLU()(decoder) decoder = BatchNormalization(axis=-1)(decoder) decoder = Conv2DTranspose(32, (3,3), padding='same', kernel_initializer='normal')(decoder) decoder = LeakyReLU()(decoder) decoder = BatchNormalization(axis=-1)(decoder) decoder = Conv2DTranspose(3, (3, 3), padding="same")(decoder) output = Activation('sigmoid', name='decoder')(decoder) # Create model object autoencoder = Model(input_model, output, name='autoencoder') # Compile the model autoencoder.compile(loss="mse", optimizer= Adam(learning_rate=1e-3)) # Fit the model history = autoencoder.fit_generator( training_set, steps_per_epoch=training_set.n // batch_size, epochs=100, validation_data=validation_set, validation_steps=validation_set.n // batch_size, callbacks = [ModelCheckpoint('models/image_autoencoder_2.h5', monitor='val_loss', verbose=0, save_best_only=True, save_weights_only=False)])

Once the model is fitted, we can try to reconstruct some images, since this is the objective of the Autoencoder:

It is time to use Latent Space to find similar images. To accomplish this, we do not need the final prediction, we need the output of an intermediate layer, specifically, the one we named `latent_space`

on the model definition. This is why it is important to name every layer in the model, so we can access quickly and transparently any layer we need.

Using the `Model`

API and the `.get_layer()`

method of the trained model is very easy to define a model with the input and output layer we choose:

```
latent_space_model = Model(
autoencoder.input,
autoencoder.get_layer(‘latent_space’).output)
```

Now every time we use the `.predict()`

method with an image as the input of this new model, we get the Latent Space as the output.

To use this approach to get similar images, we need to *predict* with the `latent_space_model`

every image, so we can compute the euclidean distance between all our saved images, and any new picture we want to find similar images.

# Load all images and predict them with the latent space model X = [] indices = [] for i in tqdm(range(len(os.listdir('./flipkart/images/images')))): try: img_name = os.listdir('./flipkart/images')[i] img = load_img('./flipkart/images/images/{}'.format(img_name), target_size = (256, 256)) img = img_to_array(img) / 255.0 img = np.expand_dims(img, axis=0) pred = latent_space_model.predict(img) pred = np.resize(pred, (16)) X.append(pred) indices.append(img_name) except Exception as e: print(img_name) print(e) # Export the embeddings embeddings = {'indices': indices, 'features': np.array(X)} pickle.dump(embeddings, open('./flipkart/image_embeddings.pickle', 'wb'))

As I already stated, we are going to find similar images by calculating the euclidean distance, so the lower the value of this calculation, the higher the resemblance of the images. We define euclidean distance as:

```
def eucledian_distance(x,y):
eucl_dist = np.linalg.norm(x - y)
return eucl_dist
```

Once we have everything defined, we can get the three most similar products of any input image. For example, if we input the following Polo shirt, we get the following 3 most similar objects:

# Image Feature Extraction

Another approach to solving this problem is to calculate the distances to the image features. Here we can use a pre-trained Deep Learning model, to extract every image features and then compare them to any new picture. In that sense, this approach is not quite different from that of the Autoencoder model, but what is very different, is the model architecture we are going to use.

In this case, we are going to use a VGG16 pre-trained model on the *imagenet* dataset

Here we are not going to train the model, we are going to extract the image features, by getting the output of the fully connected layer (named `fc1`

).

We define the following class to extract the features of the images

from tensorflow.keras.preprocessing import image from tensorflow.keras.applications.vgg16 import VGG16, preprocess_input from tensorflow.keras.models import Model import numpy as np class FeatureExtractor: def __init__(self): # Use VGG-16 as the architecture and ImageNet for the weight base_model = VGG16(weights='imagenet') # Customize the model to return features from fully-connected layer self.model = Model(inputs=base_model.input, outputs=base_model.get_layer('fc1').output) def extract(self, img): # Resize the image img = img.resize((224, 224)) # Convert the image color space img = img.convert('RGB') # Reformat the image x = image.img_to_array(img) x = np.expand_dims(x, axis=0) x = preprocess_input(x) # Extract Features feature = self.model.predict(x)[0] return feature / np.linalg.norm(feature)

Once we get the output of every image, we can choose a picture and get the top 3 most similar images. If we compare the same Polo shirt we used with the Autoencoder model, we get the following results:

As we can see, these results are not so different from the previous approach.

# Conclusion

In this post, we compared two different approaches to develop an image search engine and get image results by using a picture as an input.

We developed an Autoencoder and an Image Feature Extraction approach and get very similar results.

### About the Author

**Luciano Naveiro**

Actuary and Data Scientist.

I love the way we can explain and model the world by using math and statistics.

Buenos Aires, Argentina

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