Multimodal Search
Multimodal search will integrate several different information sources and data modalities (e.g., text, images, audio, etc.) to enable users to search for and retrieve information. This approach effectively combines information from multiple sources to provide more multifaceted and richer search results.
The characteristics of multimodal search are as follows
- Integration of information: Combining information from different data modalities can provide deeper insights. For example, when combining text and image data to search for products, both product features and appearance can be considered.
- Content enrichment: Multimodal search improves the user experience because it can leverage multiple media formats to provide information, including images, audio, and video, as well as text.
- Personalization: By combining information from different sources, search results and recommendations can be customized for each user.
- Complex query support: Multimodal search can help users find information that is not available from a single source alone. For example, text and images can be combined to search for photos taken at a specific location.
- Integration with Machine Learning: Multimodal search can also help to leverage machine learning algorithms to analyze information that combines multiple modalities to determine relevance.
Implementations of multimodal search typically use a combination of Elasticsearch functionality and plug-ins, where Elasticsearch is used as a tool to integrate and index data from different modalities and to search for relevance among modalities. It is also possible to combine methods such as machine learning algorithms and similarity scoring to achieve multimodal search.
Multimodal search using Elasticsearch
Elasticsearch is a fast and scalable full-text search engine that provides very powerful search capabilities for text data. The following are some general steps and points to consider.
- Data preparation and indexing: Elasticsearch needs to store different modal data in appropriate formats. Textual data can usually be indexed as is, but when indexing non-textual data such as images or audio, feature vectors must be extracted in an appropriate manner.
- Combining multimodal feature vectors: Combine feature vectors from different modalities to create a multimodal feature vector. This feature vector integrates the information of each modal, and possible methods for combining feature vectors include vector concatenation and weighted averaging.
- Submitting data to Elasticsearch: The multimodal feature vector is submitted to Elasticsearch as an index. For text data, treat it as a normal document, and for non-text data, store the feature vectors in the appropriate fields.
- Create a search query: Create a query for multimodal search. This will be a combination of the text portion and the feature vector portion; the Elasticsearch query DSL can be used to combine the full-text search query for the text portion and the similarity calculation for the feature vector portion.
- Ranking and displaying search results: Search results returned by Elasticsearch are ranked based on scores. This is used to display the best multimodal search results to the user. Since the results are a combination of textual and non-textual data, results that contain useful information for the user are displayed at the top of the list.
Through the above steps, multimodal search can be realized using Elasticsearch. For more information on using Elasticsearch plug-ins, see ” See “Using ElasticSearch Plug-ins and Implementation Examples” for more information on using Elasticsearch plug-ins.
Multimodal search combined with machine learning algorithms
By combining machine learning algorithms and similarity scoring, it is possible to construct more advanced multimodal search systems. The general methods are described below.
- Extracting and combining feature vectors: As described in “Various feature engineering methods and their implementation in python” TF-IDF, Word2Vec, BERT described in “BERT Overview, Algorithms, and Example Implementations“, and other techniques are used for text, convolutional neural networks (CNN) described in “Overview of CNN and examples of algorithms and implementations” are used for images, acoustic features are extracted for audio, etc. For images, a convolutional neural network (CNN) is used, and for audio, acoustic features are extracted, etc., to extract the appropriate feature vector for each modal (text, image, audio, etc.). These feature vectors are combined to create a multimodal feature vector.
- Similarity computation and scoring: To compute the similarity between multimodal feature vectors, use an appropriate similarity measure (e.g., cosine similarity, Euclidean distance, etc.) from the various similarity measures described in “Similarity in Machine Learning“. The similarities computed for each modal are combined to compute the final multimodal similarity score.
- Application of Machine Learning Algorithms: Machine learning algorithms can be used to improve the accuracy of multimodal search. For example, random forests as described in “Overview of Decision Trees with Applications and Examples“, support vector machines as described in “Overview of Support Vector Machines with Applications and Examples“, and deep learning as described in “Overview of python Keras with Examples of Application to Basic Deep Learning Tasks“. The query is then used to learn the relationship between the input query and each data modal, including deep learning.
- Integration and Ranking: Integrate the scores obtained from each modal and calculate an overall multimodal score. Based on the obtained scores, search results are ranked and presented to the user. In order to effectively present multimodal information to users, it is necessary to search and display information along temporal and spatial axes, as described in “Reference Books on Search Technology” and “User Interface for Information Retrieval” as well as to personalize the information.
- Reinforcement Learning: Integrating feature vectors of multimodal information is a complex problem, and as described in “Why Reinforcement Learning? Application Examples, Technical Issues, and Solution Approaches” it is also effective to use reinforcement learning to adjust the ranking of search results. As an approach to these issues, the system itself can adjust the scoring method based on the results and evaluations selected by the user using deep reinforcement learning as described in “Overview of Weaknesses and Countermeasures of Deep Reinforcement Learning and Two Approaches to Improve Environment Recognition“. The system itself can adjust the scoring method based on the results and ratings selected by the user.
See also “Elasticsearch and Machine Learning” for more information on the combination of Elasticsearch and machine learning.
Algorithms used for multimodal search
Multimodal search involves a variety of algorithms used to combine multiple modalities (e.g., images, text, and audio) to retrieve information. Some common algorithms are described below.
- Cosine Similarity: Similarity scoring is a method that uses a vector space model to evaluate the relevance between modalities, converting the data of each modality into a vector and calculating the cosine similarity between the vectors to determine relevance.
- Fusion between modalities: This is a method that integrates features from different modalities and projects them into a common representation space, e.g., text and image features are projected into a common low-dimensional space, where similarity is computed. architectures such as Autoencoders and Siamese Networks are used.
- Cross-Modal Retrieval: This is a method that takes query information in one modality as input and outputs information in a different modality, such as a method for retrieving relevant images when the query is text, and vice versa for retrieving text from images.
- Multimodal learning: Algorithms for learning multiple modalities simultaneously, e.g., learning a common representation using text and image data simultaneously, and performing search based on that representation. Analysis (DCCA).
- Applications of the Transformer Model: With recent advances in natural language processing, the Transformer Model described in “Overview of Transformer Models, Algorithms, and Examples of Implementations“ is being used in modalities other than text. There are Transformer architectures that have been extended to process modality-specific information.
Application Examples of Multimodal Search
Multimodal search has been widely applied in various domains. Some examples of applications are described below.
- E-commerce: Combining images and text in product searches can help users find products more easily. For example, if a user types in the text “blue dress,” products containing images of blue dresses will be displayed.
- Social Media: On social media platforms where users post text and images, it is important to search for content from both text and images. For example, a photo taken at a particular location can be searched for by both text and image.
- Web search: When users search for information on a specific topic, both text and images can provide more detailed information.
- Medical image analysis: In the medical field, images and textual information are sometimes combined to diagnose medical conditions and suggest treatments. This would be the case, for example, when X-ray images are combined with clinical data to diagnose a disease.
- Automated driving: In automated driving vehicles, information from multiple modalities, such as camera images and sensor data, must be combined to understand the vehicle’s surroundings and perform appropriate operations.
- Manufacturing: In the manufacturing industry, multimodal search is used for quality control and inspection of products from different data sources such as image data, sensor data, and text information.
These are only a few examples; in practice, multimodal search is used in many different areas. Integrating information from different modalities provides richer information, improves the user experience, and enables efficient data analysis.
Finally, we will discuss examples of these implementations. We begin with semantic search, which searches for similarities between strings.
Example implementation of semantic search using Elasticsearch Search
An example implementation of Semantic Search using Elasticsearch is shown below. Semantic Search is a method to search for similar content considering the meaning and context of text data.
- Text Preparation: Prepare the text data to be searched. This is a set of texts to be searched, such as documents, sentences, blog posts, etc.
- Indexing text data: Index the text data in Elasticsearch. The text content and attributes are mapped to the appropriate fields and indexed.
- Vectorization: Select a method to vectorize text data, using a model such as Word2Vec, Doc2Vec, or BERT to convert the semantic representation of the text into vector data.
- Indexing vector data: add text data to the index as vector data. The text ID and the corresponding vector data are stored in the index.
- Query vectorization: Input a search query and convert it to vector data. The same vectorization technique is used to convert the query meaning into a vector representation.
- Similarity Search: Calculate the similarity between the query vector and the vector data in the index. Evaluate similarity using measures such as cosine similarity between vectors.
- Retrieve results: Retrieve the text data in the index in order of increasing similarity. This returns semantically related text as search results.
- Display results: Display the retrieved text data to the user in an appropriate manner. It is common to display search results in a ranking or card format.
In this example implementation, the text data is converted into a semantic vector representation and similarity search is performed using vector scoring. It is important to select the vectorization method and parameters according to the project requirements to improve the accuracy of the semantic search.
Next, the implementation of image information retrieval is described.
Example implementation of image search using Elasticsearch Search
An example implementation of image search using Elasticsearch is shown below. Image retrieval will generally utilize Elasticsearch’s vector scoring plug-in to index image features as vector data and perform similarity searches.
- Image feature vectorization: use convolutional neural networks (CNNs) to convert images into feature vectors. For example, common CNN architectures (VGG, ResNet as described in “About ResNet (Residual Network)”, MobileNet as described in “About MobileNet“, etc.) are used to extract image features and represent them as high-dimensional vectors.
- Index vector data: Use Elasticsearch’s vector scoring plugin to index feature vectors. Each image is stored in the index as a unique ID and corresponding feature vector.
- Image upload and vector indexing: When an image is uploaded, it is passed through the CNN to generate a feature vector, which is added to Elasticsearch along with the feature vector as an index.
- Query image feature vectorization: The query image to be searched is converted into a feature vector in the same way.
- Similarity search: Use the feature vectors of the query image to calculate the similarity with the vector data in Elasticsearch. Using the vector scoring plugin, calculate the similarity between the query vector and each image vector to retrieve similar images.
- Display the results: Retrieve information such as IDs and URLs of similar images returned from Elasticsearch and display them to the user.
In this example implementation, the image features are vectorized and indexed into Elasticsearch, and the vector scoring plugin is used to perform similarity search. However, feature vectorization of image data and vector scoring settings require careful coordination, and specific models and parameters should be selected according to project requirements.
Finally, we describe a multimodal search that fuses image and text data.
Implementation of multimodal search using ElasticSearch
Multimodal search is a search technique that combines several different modalities (images, text, audio, etc.) When using Elasticsearch to implement multimodal search, it is common to use a combination of Elasticsearch features and plug-ins. The following is an example of implementation of multimodal search. An example implementation of multimodal search is shown below.
- Data collection and indexing: Data of different modalities (images, text, etc.) are collected and indexed in Elasticsearch for each modality. For example, for text data, create an index for text, and for image data, create an index for images.
- use of Elasticsearch plug-ins: Elasticsearch provides plug-ins to integrate different modalities for search. By using these plug-ins, multimodal search can be realized.
- Elasticsearch Vector Scoring Plugin: Allows integrated evaluation of feature vectors of different modalities using vector scoring.
- Elasticsearch Join Plugin: Plugin for maintaining relevance between modalities, supporting joins and associations between different indexes.
The following is an example implementation of a multimodal search using the Elasticsearch Vector Scoring Plugin, using a Python script. In this example, the search combines both text and image data.
from elasticsearch import Elasticsearch
# Connecting to Elasticsearch
es = Elasticsearch(["http://localhost:9200"])
# Text to be searched as a query
query_text = "example query"
# Multimodal search using Elasticsearch's Vector Scoring Plugin
search_results = es.search(index="text_index,image_index", body={
"query": {
"script_score": {
"query": {"match": {"text_field": query_text}},
"script": {
"source": "cosineSimilarity(params.queryVector, 'image_vector_field') + _score",
"params": {
"queryVector": [0.1, 0.2, ...] # Text feature vector
}
}
}
}
})Reference Information and Reference Books
For information on search technology in general, see “About Search Technology. For natural language processing, see “Natural Language Processing Technologies“; for image information, see “Image Information Processing Technologies.
Reference books include “From Deep Learning to Multimodal Information Processing.”
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