Blackthorn: Large-Scale Interactive Multimodal Learning

This paper presents Blackthorn, an efficient interactive multimodal learning approach facilitating analysis of multimedia collections of up to 100 million items on a single high-end workstation. Blackthorn features efficient data compression, feature selection, and optimizations to the interactive learning process. The Ratio-64 data representation introduced in this paper only costs tens of bytes per item yet preserves most of the visual and textual semantic information with good accuracy. The optimized interactive learning model scores the Ratio-64-compressed data directly, greatly reducing the computational requirements. The experiments compare Blackthorn with two baselines: Conventional relevance feedback, and relevance feedback using product quantization to compress the features. The results show that Blackthorn is up to 77.5× faster than the conventional relevance feedback alternative, while outperforming the baseline with respect to the relevance of results: It vastly outperforms the baseline on recall over time and reaches up to 108% of its precision. Compared to the product quantization variant, Blackthorn is just as fast, while producing more relevant results. On the full YFCC100M dataset, Blackthorn performs one complete interaction round in roughly 1 s while maintaining adequate relevance of results, thus opening multimedia collections comprising up to 100 million items to fully interactive learning-based analysis

Exquisitor at the Lifelog Search Challenge 2020

We present an enhanced version of Exquisitor, our interactive and scalable media exploration system. At its core, Exquisitor is an interactive learning system using relevance feedback on media items to build a model of the users' information need. Relying on efficient media representation and indexing, it facilitates real-time user interaction. The new features for the Lifelog Search Challenge 2020 include support for timeline browsing, search functionality for finding positive examples, and significant interface improvements. Participation in the Lifelog Search Challenge allows us to compare our paradigm, relying predominantly on interactive learning, with more traditional search-based multimedia retrieval systems

A learned lexicon-driven paradigm for interactive video retrieval

Abstract—Effective video retrieval is the result of an interplay between interactive query selection, advanced visualization of results, and a goal-oriented human user. Traditional interactive video retrieval approaches emphasize paradigms, such as query-by-keyword and query-by-example, to aid the user in the search for relevant footage. However, recent results in automatic indexing indicate that query-by-concept is becoming a viable resource for interactive retrieval also. We propose in this paper a new video retrieval paradigm. The core of the paradigm is formed by first detecting a large lexicon of semantic concepts. From there, we combine query-by-concept, query-by-example, query-by-keyword, and user interaction into the MediaMill semantic video search engine. To measure the impact of increasing lexicon size on interactive video retrieval performance, we performed two experiments against the 2004 and 2005 NIST TRECVID benchmarks, using lexicons containing 32 and 101 concepts, respectively. The results suggest that from all factors that play a role in interactive retrieval, a large lexicon of semantic concepts matters most. Indeed, by exploiting large lexicons, many video search questions are solvable without using query-by-keyword and query-by-example. In addition, we show that the lexicon-driven search engine outperforms all state-of-the-art video retrieval systems in both TRECVID 2004 and 2005. Index Terms—Benchmarking, concept learning, content analysis and indexing, interactive systems, multimedia information systems, video retrieval. I

Search and Retrieval—Search process

www.mediamill.nl In this technical demonstration we show the current version of the MediaMill system, a search engine that facilitates access to news video archives at a semantic level. The core of the system is a lexicon of 436 automatically detected semantic concepts. To handle such a large lexicon in retrieval, an engine is developed which automatically selects a set of relevant concepts based on the textual query and example images. The result set can be browsed easily to obtain the final result for the query