Furniture Matching Engine Evaluation: Benchmarking Geometric and Keypoint Retrieval on Held-Out Product Imagery

Furniture Matching Engine Evaluation: Benchmarking Geometric and Keypoint Retrieval on Held-Out Product Imagery

Authors: Dimanjan Dahal, Kushal Shrestha, Gemini Antigravity
Engine Under Test: Furniture Visual Matching Engine | Domain: Furniture E-Commerce
Catalogue Size: 161 Reference Items | Test Set: 26 Held-out Product Images (asia_furnitures/test/)
Evaluation Date: 2026-09-09 14:38 | Document Classification: Computer Vision Benchmark

1. Executive Summary

Visual search and product matching engines allow shoppers to upload customer photos or social media snapshots to identify matching inventory in an e-commerce catalogue.

This evaluation assesses the baseline performance of a multi-feature visual matching engine across 26 held-out test product images from the asia_furnitures/test/ collection evaluated against a production catalogue of 161 furniture products. The metric evaluated is category retrieval: whether the top-ranked match belongs to the identical furniture category as the query (exact duplicate photos were excluded from the catalogue to measure generalization).

Primary Benchmark Results

2. Methodology & Feature Extraction Pipeline

The visual retrieval pipeline computes a composite similarity score between query images and precomputed catalogue feature caches (.npz):

+-----------------------------------------------------------------------------------------+
|                        FURNITURE VISUAL RETRIEVAL PIPELINE                              |
|                                                                                         |
|  Query Image ---> [1. Line Segment Detector (LSD)] ----> Weight: 0.40                   |
|              ---> [2. SIFT / Affine Keypoint Matching] -> Weight: 0.35  ==> Composite   |
|              ---> [3. Material & Color Histograms] ----> Weight: 0.25      Confidence    |
|                                                                                         |
|  Composite Score = 0.40 * Line + 0.35 * Affine + 0.25 * Material                        |
|  Production Gate: If Composite Score >= 45.0% -> Confirm Match to User                   |
+-----------------------------------------------------------------------------------------+

Evaluation Protocol

1. Load Query: Load each query image from asia_furnitures/test/.
2. Feature Extraction: Extract LSD line geometry, affine-invariant SIFT keypoint descriptors, and HSV material color histograms.
3. Catalogue Comparison: Compare query vectors against all 161 catalogue items using cached .npz representations.
4. Rank & Gate: Rank all catalogue items by composite confidence score; designate rank #1 as the primary prediction.
5. Validation Check: Mark as PASS if category(top_match) == category(query); compare score against the 45.0% confirmation gate.

3. Per-Category Performance Breakdown

Evaluation across the 8 distinct furniture categories revealed stark performance disparities:

4. Qualitative Analysis: Successes vs. Failure Modes

4.1 Images That Worked (Correct Top-1 Category: 10 / 26)

• carbinet_48.jpg (PASS): Matched *Cabinet 43* (SKU-FURN-CARBINET-43) with 94.5% confidence. Vertical structural dividers and rectangular doors matched the catalogue item geometry.
• lamp_14.jpg (PASS): Matched *Lamp 13* (SKU-FURN-LAMP-13) with 100.0% confidence. Distinctive vertical pole silhouette and conical shade geometry yielded an exact top match.
• sofa_26.jpg (PASS): Matched *Sofa 4* (SKU-FURN-SOFA-4) with 87.5% confidence. Distinctive horizontal cushion boundaries and dark upholstery matched.
• chair_61.jpg (PASS): Matched *Chair 60* (SKU-FURN-CHAIR-60) with 85.7% confidence. Four-legged affine keypoint constellation aligned accurately.
• makeup_chair_02.jpg (PASS): Matched *Makeup Chair 01* (SKU-FURN-MAKEUP-CHAIR-01) with 80.5% confidence. Circular cushion signature and metallic stem were recognized.

4.2 Images That Failed (Wrong Top-1 Category: 16 / 26)

• armchair_46.jpg (FAIL - Expected: Armchair, Top Match: Lamp 12 at 82.7%): The straight armrests of the armchair produced parallel line segments that mapped onto the parallel column stand of a floor lamp.


• armchair_48.jpg (FAIL - Expected: Armchair, Top Match: Lamp 8 at 91.3%): Extreme false confidence (91.3%) caused by background wall lines and lamp shade contours overpowering the armchair profile.


• carbinet_47.jpg (FAIL - Expected: Cabinet, Top Match: Drawer Next To Bed 40 at 77.6%): Wood grain texture and drawer handles matched bedside drawers rather than tall standing cabinets.


• photo_frame_02.jpg (FAIL - Expected: Photo Frame, Top Match: Cabinet 45 at 96.5%): Rectangular frame edges triggered near-perfect line segment correlation with the rectangular frame of a glass cabinet door.

5. Full Appendix: Comprehensive Query Retrieval Log

6. Intelligent Analysis: Geometric vs Semantic Computer Vision

+----------------------------------------------------------------------------------+
|                   THE SEMANTIC GAP IN GEOMETRIC VISUAL SEARCH                    |
|                                                                                  |
|  Low-Level Features (LSD, SIFT):                                                |
|  - Sees 4 straight vertical lines & 2 rectangles                                 |
|  - Cannot tell if lines belong to: (A) Lamp Stand, (B) Armchair, or (C) Door     |
|                                                                                  |
|  Neural Embedding (CLIP / DINOv2):                                               |
|  - Encodes conceptual semantics: "Soft upholstered armchair for living room"     |
|  - Invariant to background lines, camera angles, and lighting                     |
+----------------------------------------------------------------------------------+

Why the 45% Confirmation Gate Failed

Because low-level line features accumulate high scores whenever edges exist, every single test query scored between 71.6% and 100.0%. Even queries that hallucinated a lamp for an armchair achieved 91.3% confidence.

Consequently, a 45% threshold provides zero discriminative filtering, resulting in a 100% false-positive confirmation rate on misclassified items.

7. Actionable Architectural Recommendations

1. Recalibrate the Decision Gate:

1. Two-Stage Hybrid Architecture (Semantic Prior + Geometric Reranking):

1. Hard-Negative Mining:

1. Clarify Engine Scope: