Segment-Based Retrieval in Multimodal Data

• Segment-based retrieval is an approach that replaces fixed document units with semantically meaningful segments—such as video intervals, text passages, or image regions—for enhanced precision.
• It employs domain-specific segmentation strategies like change-point detection, edge detection, and topic drift analysis to generate targeted and context-aware segments.
• By enabling retrieval at the segment level, the method improves precision and recall while reducing computational overhead, benefiting applications in multimedia, robotics, and legal search.

Segment-based retrieval generalizes conventional information retrieval by replacing fixed, document-level units with semantically or physically meaningful segments as the atomic retrieval targets. Instead of retrieving entire documents, images, or videos, segment-based retrieval systems organize content into temporally, spatially, or topically local segments—such as video intervals, text passages, 3D point-cloud segments, or image regions—and rank or localize them directly in response to queries. This enables more precise retrieval, more flexible evaluation of partial matches, and better modeling of user intent, with applications spanning long-form multimedia retrieval, robotics, legal search, scientific literature mining, and beyond.

1. Formal Foundations: Defining Segment-Based Retrieval

In canonical segment-based retrieval, each result is indexed as a tuple $(d, s, e)$—for instance, a temporal segment in a multimedia collection where $d$ denotes the parent document (such as a video file or a source text), $s$ and $e$ represent start and end points (e.g., seconds in a video, offsets in text, or coordinates/indices in a point cloud) with $s < e$ (Aly et al., 2013). The retrieval output is an ordered list of such segments, rather than entire documents.

The concept generalizes naturally to other domains:

• Temporal segments in video (e.g., subshots, events) (Aly et al., 2013, Jiang et al., 2023, Kim et al., 2024).
• Spatial segments in images or 3D point clouds (Dubé et al., 2019, Garg et al., 2024, Garg et al., 2024).
• Logical/textual segments in long documents (passages, rhetorical units, sections) (Chen et al., 2022, Nigam et al., 1 Aug 2025, Dong et al., 2023).
• Subgraphs or scene-graph triplets in structured image queries (Suprem et al., 2018, Sciascio et al., 2011).
• Dynamically coherent streams in audio or video, segmented by semantic boundaries (Chen et al., 10 Nov 2025).

Evaluation metrics must be adapted to handle issues of overlap, redundancy, and user attentional limits. For example, binary relevance can be defined based on temporal overlap, binned intervals, or user-tolerance windows, and classic metrics such as Precision@n and MAP are reparametrized to operate over segment lists (Aly et al., 2013).

2. Methodological Building Blocks

2.1 Segmentation Strategies

Segmentation precedes retrieval and is domain-dependent:

• Temporal segmentation: Change-point detection (scene boundaries (Kim et al., 2024)), uniform slicing, keyframe-driven partitioning (Jiang et al., 2023).
• Spatial/image segmentation: Edge detection, region-growing, clustering, and semantic instance masks (e.g., SAM (Garg et al., 2024), COCO (Shen et al., 2021), mean-shift for ancient seal characters (Li et al., 2020)).
• Text segmentation: Topic-drift algorithms (TextTiling (Dong et al., 2023)), rhetorical-role labeling (Hierarchical BiLSTM-CRF (Nigam et al., 1 Aug 2025)), or maximum-length chunking (Chen et al., 2022).

2.2 Segment Representation and Indexing

Segments are encoded into compact representations tailored to their data type:

• CNN-based voxel-grid descriptors and neighborhood pooling for 3D map segments (Dubé et al., 2019, Garg et al., 2024).
• VLAD aggregation over masked features for SuperSegments in image place recognition (Garg et al., 2024).
• Neural embeddings via segment-level or multi-view transformers for text and video (Chen et al., 2022, Kim et al., 2024, Lei et al., 2021, Jiang et al., 2023).
• Atomic neural interaction values for term–segment pairs, supporting plug-and-play retrieval scoring (Dong et al., 2023).

Segment-level inverted indexes aggregate these representations to enable efficient querying, sometimes supporting multimodal retrieval by aligning visual and textual embeddings (Kim et al., 2024, Lei et al., 2021).

3. Retrieval, Scoring, and Evaluation

Retrieval reduces to scoring segments against either a query segment or, in cross-modal setups, a multimodal query:

• For each candidate segment, compute similarity to the query—using vector distance, neural interaction, or graph-based compatibility (scene graphs, topological maps) (Suprem et al., 2018, Garg et al., 2024).
• Aggregation can be at the segment level (max-pooling, similarity-weighted voting, neural scoring) or rolled up to higher-level units (documents, images) via segment-level evidence (Chen et al., 2022, Garg et al., 2024).

Evaluation metrics are adapted to segment granularity:

• Overlap-, bin-, or tolerance-based relevance definitions regulate scoring, penalizing double-counts and adjusting for annotation or user patience (Aly et al., 2013).
• Metrics such as P@n, MAP, Recall@K, and segment-level F1 (for temporal alignment), are calculated on returned segment lists with careful accounting for overlap and redundancy (Aly et al., 2013, Jiang et al., 2023, Kim et al., 2024).

Table: Example Adapted Metrics for Segment-Based Retrieval (Aly et al., 2013)

Metric	Overlap	Binned	Tolerance-to-Irrelevance
P@5	0.70	0.60	0.533
P@10	0.657	0.56	0.453
MAP	0.30	0.159	0.10

4. Applications Across Modalities and Domains

Segment-based retrieval architectures have enabled advances in diverse domains:

• Video hyperlinking and moment localization: Locating segments that fulfill search or QA queries in large, temporally-rich multimedia corpora (Aly et al., 2013, Kim et al., 2024, Lei et al., 2021, Chen et al., 10 Nov 2025).
• Robotics and navigation: Place recognition, loop closure, and segment-based mapping from sensor data or images for robust robot localization and planning (Dubé et al., 2019, Garg et al., 2024, Garg et al., 2024).
• Text and legal IR: Retrieval of semantically relevant passages or rhetorical segment combinations (e.g., Facts+Reasoning) in legal precedent or scientific literature, improving recall and ranking over document-level queries (Chen et al., 2022, Nigam et al., 1 Aug 2025).
• Image and video retrieval: Scene-graph–based image retrieval, co-segmentation for fine-grained copy/instance search, and segment-aware object or pattern mining (Suprem et al., 2018, Shen et al., 2021, Sciascio et al., 2011).
• Streaming video QA: Real-time querying over online video streams by dynamically indexing and retrieving segment-level key–value caches tied to meaningful semantic partitions (Chen et al., 10 Nov 2025).

5. Empirical Findings and Operational Considerations

Experiments consistently show segment-based retrieval surpasses unsegmented or coarse-grained approaches in precision, recall, and user relevance:

• Segment-level neural indexing (SEINE) supports up to 28× speed-up in neural IR inference with minimal loss in MAP (Dong et al., 2023).
• In video QA and long-form video analysis, segment-based targeted retrieval yields higher QA accuracy under equivalent resource constraints than uniform frame or chunk-based methods (Kim et al., 2024, Chen et al., 10 Nov 2025).
• Temporal alignment and segment similarity detection with self-supervised keyframe extraction achieve F1 improvements (up to +4.3 points) and 5–10× storage/latency reductions compared to uniform sampling (Jiang et al., 2023).
• Segment-based legal search, extracting rhetorical roles, yields higher MAP and MRR (e.g., MAP 0.3783 for segment queries vs 0.3484 full-document) and improved citation recall (Nigam et al., 1 Aug 2025).
• Place recognition by segment-level retrieval (SegVLAD) narrows the gap from partial overlap under strong viewpoint shift, achieving consistent +2–9% recall gains over global retrieval (Garg et al., 2024).

6. Limitations, Generalizability, and Open Challenges

Practitioner control of segmentation parameters—bin size, tolerance window, segment length—can influence both retrieval effectiveness and metric interpretability (Aly et al., 2013, Chen et al., 10 Nov 2025). Overly coarse bins or excessive merging may suppress fine localization, while overly fine segmentation can increase computational burden or fragment relevance signals. The choice of relevance function (overlap, binning, tolerance) must match user interaction models and evaluator goals; each has trade-offs regarding double-counting, boundary precision, and modeling of incrementally revealed content.

Methods described are broadly generalizable: segment-based metrics, indexing schemes, and neural architectures apply directly to speech, audio, XML passage, music segment, and graphical structure retrieval (Aly et al., 2013, Qiao et al., 2024, Suprem et al., 2018). Segment granularity and representation design should be tuned to the modality and downstream task.

A plausible implication is that ongoing progress will require:

• Adaptive segmentation schemes learned end-to-end for diverse modalities.
• Better integration of user models (e.g., attention, patience, navigation goals) into both metrics and interaction functions.
• Standardization of evaluation protocols in multi-segment settings, including handling of ambiguity, redundancy, and intent drift.

7. Summary and Outlook

Segment-based retrieval provides a principled and practical framework for fine-grained, semantically meaningful retrieval in complex multimodal, multidocument, or long-form settings. By making segments the primary retrieval granularity and carefully adapting data representation, indexing, scoring, and evaluation, these systems address the limitations of monolithic document- or whole-image approaches. The recurring themes—granular decomposition, compositional query/answer structures, segment-centric indexing, and tailored metric adaptations—are now foundational in a wide array of research and operational systems, from video-LLMs and robotics to scientific and legal search (Aly et al., 2013, Chen et al., 2022, Dong et al., 2023, Kim et al., 2024, Garg et al., 2024, Nigam et al., 1 Aug 2025, Dubé et al., 2019, Jiang et al., 2023, Shen et al., 2021, Chen et al., 10 Nov 2025).