Multiplex Visual Search Engine (mViSE)

• mViSE is a retrieval system that supports interactive, high-throughput search across richly annotated, multimodal visual datasets using modular representation learning.
• It integrates domain-specific indexing and fusion in shared embedding spaces to efficiently handle diverse modalities such as medical imaging, fashion, and cultural heritage.
• Quantitative evaluations reveal high accuracy, sub-second query latency, and scalable performance, making mViSE effective for both exploratory and specialist workflows.

A Multiplex Visual Search Engine (mViSE) is a class of retrieval systems designed to support interactive, high-throughput search across richly annotated, multi-channel or multimodal visual datasets. mViSE frameworks have been independently developed for domains such as multiplex immunohistochemistry (IHC), multiplex immunofluorescence (mIF), fashion and interior design, open-domain sketch-based retrieval, mobile object identification, and cultural heritage iconography. Architectures share core principles: modular representation learning for separate input modalities, fusion in a shared embedding or semantic space, scalable approximate nearest-neighbor search, and domain-specific interfaces for exploratory or specialist workflows. The following sections review key system architectures, learning strategies, fusion/formalization techniques, operational workflows, quantitative evaluations, and field-specific deployment considerations.

1. System Architectures and Indexing Strategies

mViSE architectures vary by application but generally follow a modularized ingestion–indexing–retrieval pipeline. In multiplex IHC search engines, the workflow comprises an offline learning phase—where small cell-centered or tissue patches are extracted, partitioned into marker panels (5–8 molecular channels each), and encoded by self-supervised panel-specific neural networks. Community detection over k-nearest-neighbor graphs in embedding space yields pseudo-labels, which are stored for fast, query-driven retrieval (Huang et al., 12 Dec 2025). In interactive VR-based medical systems, H&E or mIF slides are streamed from edge devices, segmented, stain-normalized, and decomposed into overlapping patches. Dense patch representations are generated via modality-bridging VAEs, optionally followed by deep Siamese/triplet networks for further discriminative refinement (Veerla et al., 5 Jan 2024).

In open-domain or e-commerce settings, content is indexed as high-dimensional embeddings (e.g., ResNet/ViT features, CLIP multimodal vectors). These are stored within fast ANN (e.g., Faiss IVF–PQ, HNSW, KD-tree), or made compatible with inverted indices as in Elasticsearch-backed mViSE implementations via vector→string tokenization (subvector-wise k-means) (Mu et al., 2018).

For mobile multi-view engines, client devices perform interest-point detection and descriptor extraction per captured view (SIFT, BoW histograms), transmitting compacted feature vectors to a server for fusion-based object retrieval (Calisir et al., 2015).

2. Representation Learning and Fusion of Modalities

At the core of mViSE systems is the learning of discriminative, compact representations for each data modality—channels, views, modalities, or domains. In highly multiplexed IHC, a divide-and-conquer approach assigns channel subgroups (panels) to separate ViT-based encoders modified with Efficient Channel Attention. These encoders are optimized in a self-supervised loop: feature extraction → kNN-graph → InfoMap clustering → cluster-contrastive + triplet loss updates, yielding embeddings that reflect structural and cellular phenotypes (Huang et al., 12 Dec 2025). For VR-enabled medical search, cross-modality alignment is handled by VAE-based encoders, mapping both H&E and mIF patches into a common latent space, with batch correction via dynamic time warping (Veerla et al., 5 Jan 2024).

Multimodal search across text and images employs parallel branches (CNN for vision, CBOW/Word2Vec for text), with fusion at either the vector concatenation or learned projection stage. DeepStyle-Siamese, for example, concatenates L₂-normalized 128-d image and text vectors, feeding the result to a joint classification/contrastive loss, thus positioning compatible products or designs close in the learned space (Tautkute et al., 2018).

Open-domain mViSE designs utilize domain-specific encoders (SE-ResNet, VGG19) for each visual domain (e.g., sketches, photos, shape renders), trained to map inputs to points on a shared D-sphere with hyperspherical semantic prototypes—often initialized from word embeddings—providing a scalable, flexible cross-domain retrieval substrate (Thong et al., 2019).

3. Similarity Metrics, Retrieval, and Aggregation

Similarity in mViSE is typically computed via cosine or Euclidean distance in embedding space. For panelized IHC retrieval, the cosine similarity between query patch and database patch vectors governs retrieval, while for communities, graph-based proximity metrics intertwine spatial and feature-based similarity. Aggregation for multi-panel queries leverages intersection of kNN graphs followed by information-theoretic clustering (InfoMap), which minimizes code length and maximizes biological interpretability (Huang et al., 12 Dec 2025).

In medical VR systems, cosine similarity in latent space is retained for ANN search, and for cross-domain engines, both cosine similarity and angular margin metrics (e.g., ArcFace) are used to enforce category separation (Thong et al., 2019). Where multimodal search is required (e.g., text + image for cultural datasets), queries are embedded using both vision–LLMs (CLIP) and TF-IDF; results are aggregated by frequency or fused for ranking (Santini et al., 2023).

Aggregation strategies in multi-view mobile settings include both early fusion (vector-level merging via sum, avg, or max pooling across views) and late fusion (aggregation at the similarity or decision level), with operators such as max-similarity or weighted ave-max shown to optimize retrieval performance (Calisir et al., 2015).

4. Interactive Interfaces and User Workflows

mViSE interfaces are designed for fluid, high-throughput retrieval. In QuPath-based IHC engines, users can select any cell or patch, pick relevant biological panels, and retrieve communities or nearest-neighbor cell/patches—results visualized as overlays on an anatomical atlas (Huang et al., 12 Dec 2025). VR-based medical mViSE supports interactive exploration of high-dimensional slides: users manipulate protein channel stacks, adjust LUTs in real-time, select regions of interest ("lasso" ROI), and annotate slides, all within a latency target of 100–200 ms (Veerla et al., 5 Jan 2024).

In cultural heritage search, web UIs allow free-text or image upload queries, return image thumbnails, and recommended Iconclass notations, with interfaces supporting hierarchical exploration, code filtering, and real-time feedback (Santini et al., 2023). For mobile multi-view object search, a parallelized capture–extract–send pipeline on the device enables rapid acquisition of object-centric views, feeding to server-side fusion and object ranking; multi-fusion choices are user-configurable per session (Calisir et al., 2015).

5. Quantitative Evaluations

Empirical validation consistently demonstrates the superiority of multiplex and multimodal retrieval over unimodal baselines:

• In brain IHC, single-cell query top-1 and top-5 accuracies reach 0.90 and 0.96, mean IoU for cortical layer delineation is 0.70, and mViSE operates at sub-second online query latency (Huang et al., 12 Dec 2025).
• VR mViSE achieves top-5 slide retrieval accuracy of 87% and mAP@5 of 0.68 on 1,000 H&E–mIF pairs, with 80% of pathologists reporting workflow speedups ≥20% and a System Usability Scale score of 83/100 (Veerla et al., 5 Jan 2024).
• DeepStyle-Siamese mViSE outperforms VSE-VGG19 baselines by 18–21% in intra-list style coherence on fashion/interior datasets (Tautkute et al., 2018).
• Open cross-domain mViSE surpasses prior SOTA in sketch-to-photo and sketch-to-shape retrieval, achieving mAP@all improvements of 4–15% over existing benchmarks (Thong et al., 2019).
• ElasticSearch mViSE, using subvector-wise clustering encoding, yields Precision@24 of ~92% at 0.3 s latency for large-scale e-commerce image search (Mu et al., 2018).
• Mobile multi-view search consistently outperforms single-view, with mAP improvement of +0.10–0.20 on diverse product/object datasets; late fusion (weighted ave-max) secures the best trade-off of accuracy and speed (Calisir et al., 2015).
• In multimodal cultural image searches, no overall preference was observed between CLIP and TF-IDF, but each is preferred in distinct search tasks (CLIP: exhaustiveness, TF-IDF: precision) (Santini et al., 2023).

6. Implementation, Scalability, and Deployment

Open-source mViSE implementations exist as QuPath plugins for IHC (Huang et al., 12 Dec 2025), VR Unity clients with Python/Flask backends for clinical slides (Veerla et al., 5 Jan 2024), and as dockerized Python/Elasticsearch microservices for large-scale commerce (Mu et al., 2018). Efficient index structures (Faiss, in-memory or disk-based ANN, subvector clustering tokenization for Elasticsearch) are required to support hundreds of thousands to millions of objects/views/channels at latencies below 1 s.

Resource requirements scale with modality/channel count, embedding dimensionality, and chosen fusion strategy; batch feature extraction and indexing are performed GPU-accelerated, but online search can be CPU- or even edge-optimized (Huang et al., 12 Dec 2025, Veerla et al., 5 Jan 2024).

Multi-domain retrieval scales linearly in the number of domains owing to independent encoder training and a fixed, shared semantic prototype set. New domains require only the addition of an encoder trained against the fixed prototypes, no retraining of prior encoders (Thong et al., 2019).

7. Limitations and Prospective Developments

Known challenges include panel selection heuristics for marker assignment in multiplex IHC, open-domain embedding drift for vision–LLMs, the need for scalable community validation in unsupervised settings, and potential accuracy–latency trade-offs in high-channel or cross-modal retrievals (Huang et al., 12 Dec 2025, Santini et al., 2023, Mu et al., 2018). Proposed extensions involve hybrid rankers fusing deep and ontology-based scores, deployment of classifier-driven re-ranking, robust user-driven feedback loops, RESTful and SPARQL APIs for external integration, and cross-vocabulary or cross-domain embedding visualizations (Santini et al., 2023, Veerla et al., 5 Jan 2024).

A plausible implication is that as datasets grow in size, modality, and annotation complexity, domain-specific but interoperable mViSE components—modular encoders, community-based aggregations, and streaming, intuitive interfaces—will be increasingly essential for scalable, interpretable, and interactive visual content discovery.