Listen, Look, and Gotcha: Instant Video Search with Mobile Phones by Layered Audio-Video Indexing *

ABSTRACT Mobile video is quickly becoming a mass consumer phenomenon. More and more people are using their smartphones to search and browse video content while on the move. In this paper, we have developed an innovative instant mobile video search system through which users can discover videos by simply pointing their phones at a screen to capture a very few seconds of what they are watching. The system is able to index large-scale video data using a new layered audio-video indexing approach in the cloud, as well as extract light-weight joint audio-video signatures in real time and perform progressive search on mobile devices. Unlike most existing mobile video search applications that simply send the original video query to the cloud, the proposed mobile system is one of the first attempts at instant and progressive video search leveraging the light-weight computing capacity of mobile devices. The system is characterized by four unique properties: 1) a joint audio-video signature to deal with the large aural and visual variances associated with the query video captured by the mobile phone, 2) layered audio-video indexing to holistically exploit the complementary nature of audio and video signals, 3) light-weight fingerprinting to comply with mobile processing capacity, and 4) a progressive query process to significantly reduce computational costs and improve the user experience-the search process can stop anytime once a confident result is achieved. We have collected 1,400 query videos captured by 25 mobile users from a dataset of 600 hours of video. The experiments show that our system outperforms state-of-the-art methods by achieving 90.79% precision when the query video is less than 10 seconds and 70.07% even when the query video is less than 5 seconds. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]. The search process can stop anytime once a confident search result is achieved. Thus, the user does not need to wait for a fixed time lag. The proposed system is characterized by its unique features such as layered audio-video indexing, as well as instant and progressive search. Categories and Subject Descriptor

Spatial and temporal representations for multi-modal visual retrieval

This dissertation studies the problem of finding relevant content within a visual collection according to a specific query by addressing three key modalities: symmetric visual retrieval, asymmetric visual retrieval and cross-modal retrieval, depending on the kind of data to be processed. In symmetric visual retrieval, the query object and the elements in the collection are from the same kind of visual data, i.e. images or videos. Inspired by the human visual perception system, we propose new techniques to estimate visual similarity in image-to-image retrieval datasets based on non-metric functions, improving image retrieval performance on top of state-of-the-art methods. On the other hand, asymmetric visual retrieval is the problem in which queries and elements in the dataset are from different types of visual data. We propose methods to aggregate the temporal information of video segments so that imagevideo comparisons can be computed using similarity functions. When compared in image-to-video retrieval datasets, our algorithms drastically reduce memory storage while maintaining high accuracy rates. Finally, we introduce new solutions for cross-modal retrieval, which is the task in which either the queries or the elements in the collection are non-visual objects. In particular, we study text-image retrieval in the domain of art by introducing new models for semantic art understanding, obtaining results close to human performance. Overall, this thesis advances the state-of-the-art in visual retrieval by presenting novel solutions for some of the key tasks in the field. The contributions derived from this work have potential direct applications in the era of big data, as visual datasets are growing exponentially every day and new techniques for storing, accessing and managing large-scale visual collections are required