openXBOW - Introducing the Passau Open-Source Crossmodal Bag-of-Words Toolkit (1605.06778v1)

Abstract: We introduce openXBOW, an open-source toolkit for the generation of bag-of-words (BoW) representations from multimodal input. In the BoW principle, word histograms were first used as features in document classification, but the idea was and can easily be adapted to, e.g., acoustic or visual low-level descriptors, introducing a prior step of vector quantisation. The openXBOW toolkit supports arbitrary numeric input features and text input and concatenates computed subbags to a final bag. It provides a variety of extensions and options. To our knowledge, openXBOW is the first publicly available toolkit for the generation of crossmodal bags-of-words. The capabilities of the tool are exemplified in two sample scenarios: time-continuous speech-based emotion recognition and sentiment analysis in tweets where improved results over other feature representation forms were observed.

• The paper introduces openXBOW, an open-source toolkit for generating crossmodal bag-of-words representations from multimodal data streams including acoustic, visual, and textual low-level descriptors.
• openXBOW offers features for preprocessing, codebook generation (like k-means), temporal segmentation, soft quantization, and supports various formats (ARFF, CSV, LIBSVM) to facilitate crossmodal signal processing.
• Experimental results demonstrate openXBOW's efficacy, outperforming the AVEC 2016 baseline for speech emotion recognition on RECOLA and matching state-of-the-art for Twitter sentiment analysis.

Overview of the openXBOW Toolkit for Crossmodal Bag-of-Words Generation

The paper "openXBOW -- Introducing the Passau Open-Source Crossmodal Bag-of-Words Toolkit" presents a novel tool designed for generating bag-of-words (BoW) representations from multimodal inputs. The openXBOW toolkit extends the traditional BoW approach used in natural language processing and adapts it to accommodate acoustic and visual low-level descriptors, enabling the creation of crossmodal histogram feature representations, which are increasingly important in areas like emotion recognition and sentiment analysis.

Context and Motivation

BoW models are well-established in NLP for document classification, where they form word frequency vectors that inform machine learning classifiers. However, the approach has inherent limitations, notably its disregard for the order of terms. Despite this, BoW has gained traction in visual (BoVW) and audio analysis (BoAW), proving effective in various applications, including acoustic event detection and music information retrieval.

The openXBOW toolkit was developed to facilitate the integration of BoW representations across different input modalities, supporting concatenation of features from different streams such as acoustic data, visual data, and text. The toolkit accommodates arbitrary numeric inputs, suggesting its flexibility and wide applicability for researchers working with multimodal data.

Toolkit Features

The openXBOW toolkit, implemented in Java and available as an open-source project on GitHub, supports multiple input and output file formats, enhancing its accessibility for the research community. It offers a range of functionalities for preprocessing and feature extraction, including:

• Support for ARFF, CSV, and LIBSVM formats.
• Normalization and standardization options for low-level descriptors (LLDs).
• Flexibility in codebook generation through methods like random sampling and k-means clustering.
• Facilities for segmenting input data temporally.
• Soft vector quantization options to enhance the TF calculations.
• Text processing techniques like n-grams and inverse document frequency weighting.

The toolkit's modular design provides researchers with a robust framework to generate comprehensive BoW features from complex multimodal datasets, fostering advancements in crossmodal signal processing.

Experimental Validation

The efficacy of openXBOW has been demonstrated in two experimental setups: time-sensitive emotion recognition from speech signals and sentiment analysis from Twitter data. Noteworthy results include:

• On the RECOLA dataset, openXBOW significantly outperformed the AVEC 2016 baseline in speech emotion recognition tasks, achieving superior concordance correlation coefficients.
• For Twitter sentiment analysis using a large corpus, the toolkit's results competently matched or slightly exceeded the reported state-of-the-art with a weighted accuracy exceeding 77%.

These results substantiate the toolkit's capacity to generate meaningful representations in diverse domains, highlighting its utility in harnessing multimodal data for predictive tasks.

Implications and Future Directions

openXBOW's introduction represents an important step in making crossmodal BoW modeling more accessible, with potential implications for enhanced multimodal analytics in fields such as affective computing, multimedia content analysis, and beyond. Furthermore, the open-source nature of the toolkit opens opportunities for community-driven enhancements.

Future developments will likely include the incorporation of more sophisticated quantization techniques, GUI improvements, and possibly enhancements that account for sequential dependencies in data streams, such as temporal feature augmentation and n-grams for numeric features. Continued updates and community engagement, as anticipated by the authors, will further solidify openXBOW's stance as a versatile tool in crossmodal research.

Overall, openXBOW holds strong potential to facilitate crossmodal research, allowing experts to efficiently extract and model multimodal data, thereby pushing the boundaries of applications reliant on comprehensive feature representation.